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Enhanced Support Vector Machine based on Metaheuristic Search for Multiclass Problems

Faculty of Information Technology and Computer Sciences, University of Sheba Region, Marib, Yemen Deptartment of Management Information Systems, College of Business Administration, Prince Sattam Bin Abdulaziz University, Saudi Arabia Deptartment of Management Information Systems, Faculty of Administrative Science, Hadramout University, Yemen Deptartment of Computer Science, Faculty of Computers and Information, Assiut Universit, Assiut 71526, Egypt Deptartment of Information Systems, Faculty of Computers and Information, Assiut University, Assiut 71526, Egypt


Introduction
Support Vector Machine (SVM) is one of the promising and effective machine learning algorithms for regression and classification problems (Tharwat, 2019), it's learning behavior depends on statistical learning theory (Cortes and Vapnik, 1995;Vapnik and Vapnik, 1998). Due to promising performance of SVM, it is applied in several domains, for example; bioinformatics (Schölkopf et al., 2004), text classification (Joachims, 2002), fault diagnosis (Zhang et al., 2015) and pattern recognition (Burges, 1998). SVM originally is designed to solve the binary classification problems. Furthermore, it is not easy to extend it for solving problems with more than two classes, known as multiclass classification problem, It is still an ongoing research area (Tharwat, 2019;Allwein et al., 2000). Usually, a decomposition strategy is employed for multiclass classification problem, in this case; the multiclass problem is partitioned into several binary sub-problems. Then, SVM is applied to each of these binary sub-problems to classify them; the outputs of these sub-problems are combined to get the class of new instances. Thus the automatically setting of binary and kernel parameters of SVM is not trivial task and it has major effect on performance and simplicity of SVM classification model. However, more analysis is needed for optimization to avoid the black and random selections of SVM parameters, which affect performance and create a complex prediction model (Tharwat, 2019;Zhang et al., 2015;Allwein et al., 2000). Therefore, SVM parameters must be determined carefully during training process to enhance prediction and select the accurate classification model. In other word, inappropriate parameters setting will leads to poor and irrelevant knowledge that degrades model performance (Burges, 1998;Allwein et al., 2000;Keerthi and Lin, 2003). In the decomposition of multiclass applications, the optimal parameter values for each of the binary SVM may differ. Generally, the empirical search for these values through a trial and error investigation method is clearly impractical. One other visible and strong solution is an investigation of the optimization techniques behaviors for SVM parameters values tuning.
This paper proposed an enhanced version of SVM based on Scatter Search (SS) optimization approach that used for automatically setting of the SVM parameters and its kernel parameters that contained in decomposition methods in multiclass prediction problem. SS approach is used due to its flexibility, since each of its elements or steps can be implemented in a variety of ways and it has acceptable degree of sophistication (Martí et al., 2006). Furthermore, SS can find solutions of a higher average quality earlier during the search more than some meta-heuristic search methods (Campos et al., 2001). Additionally, using some optimization algorithms usually loss some of relevant solutions and trap in local optimum problem which ignore or do not reaches best solutions in many cases. Thus, it is inevitable to apply one of meta-heuristic optimization strategy such as SS to find global optimum solution.
The rest of the paper is organized as follows; the next section presents reviews of related works and gives basic knowledge of SVM. Section 3 describes the stages of proposed method and section 4 reports and discusses the experiments and results for two collections of datasets. Finally, the conclusion makes up section 5.

Related Works
The SVM is an effective learning method was initially proposed to solve binary classification problem and maturated for multiclass classification of some application systems. While an inappropriate parameters, setting is a major problem of SVM that possibly result in poor and irrelevant information that degrades SVM model performance. Recently, several parameters tuning approaches for SVM have been proposed, but they have mainly been focused on trial and error investigation methods and applied to binary classification. However another solution is possible which set SVM parameters based on meta-heuristic search algorithms that locate best values (best solution) for parameters in global way. The following briefly discuss the basic concept of SVM with its parameters that require setting optimization and provide overview of several approaches of parameters tuning for SVM.
The basic idea of SVM is to implicitly map the training data into a high-dimensional feature space. A hyper-plane is constructed in this feature space, which maximizes the margin of separation between the hyperplane and the points lying nearest to it (called the support vectors) (Tharwat, 2019;Cortes and Vapnik, 1995;Vapnik and Vapnik, 1998;Palaniswami et al., 2000). The hyper-plane can be used as a basis for classifying vectors of uncertain type. In a case of linearly separable data, the problem of two-category classification is stated as the follows.
Suppose that there are N training pair (xi, yi), where xi is an object and yi is a class label (±1) and i = 1 to N. The hyper-plane is defined by a discriminate function as follows: where, the vector w of dimension equal to that of x and scalar b are chosen such that: Classification of an unknown vector x into class label y(±1) is done using the discriminate function and defined as: In the non-separable case, the basic idea in design the nonlinear SVM is to map input vectors x ∈R n into vectors Φ(x) of a higher dimensional feature space with m features (where Φ: R n  R m ). Then, solve a linear classification problem in this new feature space. To avoid an explicit representation Φ(x) of the feature space, kernel trick is applied. Kernel function K(X, Z) = (Φ(X) · Φ(Z)) is a function that perform mapping from input space into higher dimension feature space. After that, a linear machine is used to classify the data in the feature space. Several kernel functions assist SVM in obtaining the optimal solution. The most frequently used as kernel functions are the Polynomial, Sigmoid, Gaussian and Radial Basis Function (RBF). The RBF and Gaussian kernels are frequently used by most studies, more details can be found in literature such as (Tharwat, 2019;Zhang et al., 2015;Faris et al., 2018;Tuba and Stanimirovic, 2017;Yin and Yin, 2016;Maglogiannis et al., 2009;Samadzadegan and Ferdosi, 2012;Jia et al., 2011;Sartakhti et al., 2012;Li-Xia et al., 2011;Chen et al., 2011;Lin et al., 2008a;2008b;Huang and Wang, 2006;Pai and Hong, 2005a;2006;2005b).
One of the major problem that face SVM is how to choose the appropriate value of it is parameters, while unsuitable setting lead to classifier with poor performance (Tharwat, 2019;Zhang et al., 2015;Keerthi and Lin, 2003). The parameters that should be optimized are the complexity parameter C, epsilon and tolerance t and the kernel function parameters, such as σ for Gaussian kernel. The parameter C determines the trade-off between the fitting error minimization and model complexity (Zhang et al., 2015;Faris et al., 2018;Tuba and Stanimirovic, 2017;Yin and Yin, 2016;Wu et al., 2007;Ren and Bai, 2010;Cherkassky and Ma, 2004;Liu and Jiang, 2008), where its value indicates the error expectation in the classification process of the sample data and it impacts the number of support vectors generated by the classifier (Liu and Jiang, 2008). The employment of a decomposition strategy in multiclass problems increases the number of parameter values to be determined, since every binary classifier deal with different classification problem and may have distinct ideal parameter values. Authors in (Lorena and De Carvalho, 2008) summarize three methods that can be followed to set the value of the parameters: I. Use default values for SVM and its kernel, where each tool of SVM may define or set values for each SVM parameters and its kernel II. Set the values manually by trial and error III. Tune the values via some optimization techniques, such as simulated annealing, particle swarm, Genetic Algorithm (GA) and many others In literature, some works were conducted to solve parameters tuning of SVM in multiclass decomposition. For instance, GA used in (Lorena and De Carvalho, 2008;Samadzadegan et al., 2010) for parameters tuning of SVM. The code matrix strategy is used in the first method for decomposition the multiclass problem. In addition, the method conducts two types of experiments: first, use different values of parameters for each binary classifier, while in the second experiments the same values of parameters are used for all binary classifiers. The authors via their experiments claim that the GA is able to get the solutions that reduce validation error rate (Lorena and De Carvalho, 2008). Since the method, proposed in (Samadzadegan et al., 2010) uses OAO and OAA methods for multiclass, as well as the method used the same values of the parameters for all binary classifiers. However, several methods are proposed in literature for finding the best values of SVM parameters and its kernel parameters, which focus only on problems with binary class. Many different techniques are employed like grid search (Hsu and Lin, 2002a;LaValle et al., 2004), GA (Pai and Hong, 2005a;2006;Ren and Bai, 2010), Simulated Annealing (SA) (Jia et al., 2011;Sartakhti et al., 2012;Lin et al., 2008a;Pai and Hong, 2006;2005b), Particle Swarm Optimization (PSO) (Lin et al., 2008b;Li-Xia et al., 2011;Ren and Bai, 2010;Sudheer et al., 2011;Lins et al., 2012) and other methods such that presented in (Zhang et al., 2015;Faris et al., 2018;Tuba and Stanimirovic, 2017;Yin and Yin, 2016;Samadzadegan and Ferdosi, 2012). Authors in (Hsu and Lin, 2002a) and (LaValle et al., 2004) use a grid search algorithm to find near optimal value of C and σ, when Gaussian kernel function is used. However, this method is time consuming and does not perform well. Another study (Lin et al., 2008a) claims that the setting search interval is a problem, where too large interval wastes computing power. On the other hand, too small interval might render a satisfactory outcome impossible. Pai and Hong (2005a) present an approach that combine GA and SVM. Their model imitates chromosome coding in their GA to generate a set of parameter values for SVM. Additionally, Wu et al. (2007) use a real-valued GA to optimize the parameters of SVM for predicting bankruptcy; the suggested technique is tested on the forecasting of financial crisis in Taiwan, where the presented results were promising. Researchers conclude that integrating the RGA with SVM is very successful. Pai and Hong (2006;Pai and Hong, 2005) also present SA approach to obtain parameter values for SVM and applied it to real data. In addition, a hybrid prediction method called SA-SVM proposed for predicting synthesis characteristics of the hydraulic valve; SA is used to optimize the parameters of SVM (Jia et al., 2011). Authors prove via experiments the strategy is applicable to forecasting the synthesis characteristics of hydraulic valve with higher accuracy rate. Lins et al. (2012) in propose method for reliability prediction, where PSO method used to solving the parameters setting problem of SVM. In (Faris et al., 2018), researchers suggested a new approach based on metaheuristic called multi-verse optimizer for tuning the parameters of SVM, the suggested method was implemented and tested on two different system architectures and the obtained results was very promising. In (Samadzadegan and Ferdosi, 2012), authors use bees algorithm to optimize the SVM parameters as well as feature selection. Also, they compared their work with other methods likes GA and grid search, their method was the best in all performance aspects. However, all of these methods depend on prior knowledge, user expertise, or experimental trial with black box of parameters values. Hence, there is no guarantee that the parameters values obtained are optimal (Tharwat, 2019;Ren and Bai, 2010).
On the other side, SS is a population-based algorithm which was first proposed by F. Glover in the 1970's (Glover, 1977), based on some results reported in 1968. However, the SS template in its final form is introduced in 1977 ( Glover, 1968a;1968b). SS has advantages such its flexibility and sophistication which make it a visible for integration with other method to find the global optimum solution for many problems. In the field of parameter setting, a few works are done using SS and oriented to binary classification problems. For instances; Lin and Chen (2012) suggest an approach to determine the parameters and feature selection for C4.5 algorithm by employing SS meta-heuristics strategy. In another research (Chen et al., 2011), SS approach was used to determine the parameters of three machine learning algorithms and performing feature selection to enhance the classification accuracy. However, these works were concerned for binary classification problems. Generally, the optimization algorithms such as GA, SS, ACO and PSO are a good approach to employ for optimal search in several fields and investigate their ways for direct mining in categorization process as a rule based categorization algorithm (Afif et al., 2020). In addition; they can be used for guidance of data mining functions to determine the best solution for functional parameters values and relevant features for predictions. They can produce several alternative parameters setting and/or multi relevant subset of prediction features through reproduction operations on its behavior to finding the best values for parameters and/or best solution for optimal features of specific search problem (Lin et al., 2008a;Ghareb et al., 2016).

The Proposed Method
The main objective of this study is to enhance SVM performance by employing SS optimization approach for automatic setting of the SVM parameters specifically with multiclass prediction problem for Lung Cancer Diagnosis after validation with standard benchmark datasets. Thus the proposed method depends on three major components; SVM with Kernel function, decomposition strategy of multiclass and SS for parameters tuning optimization. The next subsections present the major components of proposed method.

SVM and Kernel Function
The SVM concept is discussed earlier in section 2, more details can be found in (Tharwat, 2019;Joachims, 2002;Zhang et al., 2015). The proposed method uses Gaussian kernel as kernel function that assist SVM to find best separation of different classes and achieves the optimal solution. The parameter of kernel function is optimized along with other SVM parameters using SS. In this study the Gaussian kernel is used because the linear kernel has been proven to be a special case of the Gaussian kernel. Also, it has few parameters rather than other kernels and it is usually numerically more stable than both polynomial and sigmoid kernels (Tharwat, 2019;Keerthi and Lin, 2003;Yin and Yin, 2016;García-Pedrajas and Fyfe, 2008). It should be noted that any other kernel function can be used and optimized but the complexity will be differ. The Gaussian kernel, K(X, Z), is illustrated in Equation (5), where mapping is introduced from input space into higher dimension feature space and σ is the kernel parameter:

Multiclass Decomposition Strategy
Solving multiclass classification problem is still an on-going research issue. There are two approaches are employed to solve multiclass classification problems using SVM. The first one includes modifying the SVM learning algorithm, often this type of modifications are not trivial and may produces costly learning algorithm (Hsu and Lin, 2002b). Second approach is the decomposition, which depends on splitting the multiclass problem into several binary subtasks. This is more frequent and common used approach. Many decomposition methods are suggested in literature. These approaches can be divided into two groups: the code matrix based approach and the hierarchical approach (Lorena and De Carvalho, 2008). This paper is only concerned with code matrix based approach. The code matrix based strategy is generally represented by matrix M with dimension K  L, where k the number of classes and L represents the number of binary classifiers that is required in multiclass solution. Every row of the matrix M has a binary code associated with one of the classes. The columns of M define binary partitions of the K classes and correspond to the labels that these classes assume the binary classifiers generation. Each element of M has value in the set −1, 0, +1. If element mij equal to +1 means that the corresponding class to row i has positive label in the induction of the classifier fj, while value -1 represent negative label and 0 for instances from class i that do not include or involve in the classification process of the classifier fj. A new data x will be classified by applying the decoding process, which based on evaluating the predictions of the l classifiers, that vector is compared to M rows. The pattern is assigned to the class whose row is closest according to some integration, researchers distance measure. Several decoding functions that can be employed in the SVM binary classifiers integration, for example; researchers in (Allwein et al., 2000) suggested the use of a decoding function depends on the margins by which the instance is classified by the binary SVM. This was the function that employed by (Lorena and De Carvalho, 2008) and this study also will be based on that function in decoding process, where it is equation is given below: where, q m represent the q th row of M matrix, while q = 1,···, k. The most popular approaches for decomposition are: One Against One (OAO), One Against All (OAA) and Error Correcting Output Codes (ECOC) (Lorena and De Carvalho, 2008;Dietterich and Bakiri, 1994). This paper focuses only on OAO and OAA approaches.
As discussed in related works (Lorena and De Carvalho, 2008;Dietterich and Bakiri, 1994), in the OAO decomposition approach, a number of binary classifiers (k(k−1)/2) are generated to classify datasets with k-classes where k >2. Every classifier is used to separate one pair of data classes (i,j), where (i ≠ j). The code matrix in this case has dimension k × k(k−1)/2. In a column representing the pair (i,j), the value of the element in i row is +1 and the value of the of the member in j row is -1, while all other elements in the column have 0 value, means that examples from the other classes do not include in this classifier. Figure 1 shows an OAO matrix for problem with four classes.
Likewise; in the OAA decomposition approach, k binary classifiers are generated to classify datasets with k-classes where k >2. Each classifier is trained to separate class i against reset classes. The representation of code matrix in this strategy is given by a matrix with k × k. All elements in the diagonal of the matrix have value +1, while -1 value for the reset elements of the matrix. Figure 2 shows OAA matrix for problem with four classes.

Solution Representation
In this study, the solution is represented as a vector with dimension equals to the number of trial solutions. Figure 3 depicts the solution representation, where P1 σ is kernel parameter while others are SVM parameters, P2 C is Complexity, P3ϵ is epsilon and P4 t tolerance. The accuracy rate of every binary classifier is used to measure the quality of solution, which called the fittness function (fit). Accuracy rate for binary and multiple classes is calculated as given in Equation (7) (Huang and Wang, 2006;Afif et al., 2020;Dietterich and Bakiri, 1994): where, True Positive (TP) is the positive cases that classified correctly as positive, True Negative (TN) is the negative cases that classified correctly as Negative, while, False Positive (FP) some cases with negative class classified as positive and False Negative (FN) are the cases with positive class classified as negative.

Scatter Search for Parameters Tuning
SS technique, unlike the most of other evolutionary algorithms, uses a small set of best solutions called a reference set which updated frequently during execution. The basic steps and components of SS can be described as follow (Chen et al., 2011;Glover, 1977;Lin and Chen, 2012

Generation Method
In this study, the diversification generation step depends on generating random values for all parameters in the solution representation. Equation (8) 1  2  1  3  1  4  1  5  2  3   2  4  2  5  3  4  3  5  4  5   , , , , , , , , , , , , , , , , , , , In the next step, the solutions combination is performed, where a number of new solutions are generated from each subset that generated in the previous step as follows: where, r1, r2 and r3 are random numbers in (0,1) and factor = 0.5. Means that three solutions will be generated from each subsets that generated, if the number of subset = 6; then 18 solutions will be generated. After that, solutions are used for model building and testing and the results will be saved in pool together with solutions in the RS in order from the best one to worst. Then, the RS is updated to has the high quality solutions; RS-Size1 solutions from the pool and the RS-Size2 diverse solutions where RS-Size1+RS-Size2 = RS-Size. The diverse solution is selected based on calculating the Euclidean distance for each solution in the RS and solutions in pool. The RS-Size2 solutions with the maximum distance are selected as diverse ones. The subset generation, solution combination and RS update steps are repeated to find the best solutions in an iterative procedure until one of the termination conditions is satisfied. This research paper defines three termination conditions, therefore the termination is activated, the optimized solutions are retrieved and the process is stopped when any of the following conditions is satisfied: i. All possible solutions for parameters value are generated for a given interval, or ii. The achieved accuracy rate is 100% by at least one solution after validation, or iii. The maximum number of iterations (MaxIteration) is reached

Experimental Results and Analysis
This section presents the experiments that conducted to validate and test the performance of the proposed integration method of SVM with SS for parameter tuning. The performance is traced and measured on several datasets in term of classifier performance, error rate and standard deviation of error rate. In addition, a comparison between the proposed method and other related methods is conducted to show the efficiency of proposed method comparing to other methods in terms of classification accuracy. Furthermore, it shows the effectiveness of SS as parameter tuning algorithm and shows its effect on SVM performance as classification method for several datasets.

Datasets and Experiments Setting
The proposed method is evaluated on two types of experimental datasets from different domains, the first domain includes 9 datasets and the second experiment is conducted on lung cancer datasets. The method performance is traced and measured on these datasets and a comparison with some related works is provided. In the first experiments, as illustrated in Table 1, nine datasets from LibSVM tool webpage (Lin and Chang, 2011) are used to verify the quality of the proposed method. In addition, Tables 2 and 3 summarize all parameters setting used in the proposed method with their assigned values. These chosen values are based on the common setting in the literatures (Chen et al., 2011;DeCoste and Wagstaff, 2000;Williams et al., 2007;Lin and Lin, 2003) and based on the conducted numerical experiments.

Results and Discussion on 9 Datasets from UCI
To guarantee valid results for making predictions regarding new data, the proposed approach use the holdout method, which is the simplest testing technique that avoids over-fitting problem (Hamel, 2011). The holdout method depends on splitting the datasets into two parts; one for training and the other for testing with size 70% and 30%, respectively. The results are listed in Table 4 and Table 5 for OAO and OAA methods on 9 datasets. Each table contains the accuracy rate for training (Acc. Training) and remainder columns contain: Accuracy rate for testing process (Acc. Testing), the number of generation when the best solution is obtained (No.Gen.Best Sol.), number of hitting the best solution (No.Hit.Best Sol.) and fitness function evaluation times (Fitness ET). The average of accuracies that achieved in training and testing phase using OAO method are 96.41%, 97.89% and the maximum and minimum are 100%,100%, 83.24%, 91.94% respectively, while the standard deviation are 6.19% and 3.18%. Moreover, the accuracy rate for training and testing phase verify the approach does not suffer from the over-fitting and under-fitting problem. This can be noted via the differences between the accuracy rate for training and testing, where the maximum difference is 9.37% and the minimum is -1.66% with 1.46% and 3.27% for the average and standard deviation, respectively for OAO method. While in the OAA method, the maximum is 5.98% and -0.31% for minimum difference, with average 0.99% and 2.52% for standard deviation. These differences for two methods are very reasonable and according to the fact that there is no large difference between the training and testing accuracy (Chen et al., 2011;Lin and Chen, 2012). Figures 4 and 5 depict these differences graphically.
Furthermore, Table 6 and Table 7 list other aspects of performance measures for the proposed method. The tables list the Error Rate for Testing (ER.Rate. TS), which is calculated by dividing the sum of errors over the times of classification or over the number of required classifiers (Lorena and De Carvalho, 2008). The second column displays the Standard Deviation of error rate for Testing (StDev. TS), the reminder columns contain in sequences the sensitivity and specificity, where they reflect the true positive rate and true negative rate, expressed as a percentage, respectively. The sensitivity and specificity also reflect how well the classifier discriminates between case with positive and with negative classes (Huang and Wang, 2006;Hamel, 2011). The last two columns list Error Rate for Training phase (ER.Rate TR) and the Standard Deviation (StDev. TR), where error rate is calculated by dividing the summing of errors for all classifiers over the number of classifiers. The average of (ER.Rate. TS) and (ER.Rate TR) for OAO method are 1.55%, 0.0358, while sensitivity and specificity that produce are 98.02%, 93.74% with standard deviation 1.92% and 10.8%, respectively. From the results, we can conclude that the outcomes of the approach are encouraged for the two methods OAO and OAA, where OAO is the best in all the performance aspects. Furthermore, it is also faster in training and seems preferable for problems with a large number of classes (Galar et al., 2011;Milgram et al., 2006). Additionally, the size of datasets that are used were differ because some of datasets have a large number of instances, which require more time especially with OAA method.     Thus, the proposed approach especially OAO performs well when using datasets with high dimensional and large number of instances. This is proved through experiments that are conducted. In order, to illustrate the performance of the proposed approach, the obtained results are compared with other published approaches (Lorena and De Carvalho, 2008;Samadzadegan et al., 2010;Blondin and Saad, 2010) as shown in the Tables 8 to 13. Tables 8 and 9 display comparisons between the method with others developed by (Samadzadegan et al., 2010), which uses GA and Grid search method to tune the parameters of SVM in multiclass decomposition. The outcomes of suggested method are the best in all methods (OAO and OAA), where the accuracy rate is increased with average are 5.95% and 10.95% in OAO method and 3.81% and 8.81% for OAA. Also, a statistical analysis is performed to prove if there any significant difference is existed in performance between the proposed method and related method (Samadzadegan et al., 2010). The statistical analysis for the performance of the approach is an important and necessary task to be conducted for evaluation. Statistics enable us to determine if there are any significant differences among the results produced by the suggested method when comparing with other related methods. Some of researches recommended that the use of nonparametric tests is good to show significant differences (Galar et al., 2011;Demšar, 2006;García et al., 2009;Garcia and Herrera, 2008). The Wilcoxon test is used to compare the outcomes; Table 10 reports the results of statistical analysis. The p value are 0.042, 0.043 for OAO method. This means that the significant differences is exist, where the produced p-value less than 0.05. Moreover, there are some differences between the proposed method and method suggested by (Samadzadegan et al., 2010) that uses the same values of the parameters for all binary classifiers in two method OAO and OAA. Also the method does not uses the code matrix decomposition strategy.
Tables 11 and 12 list the comparisons between testing error rate and standard deviation of the proposed method and method developed by Lorena and De Carvalho (2008), where the code matrix were used by two methods. The major difference is that their suggested method divide the datasets into two groups; one part for validation and the other for testing using hold out method and cross validation fold method. Also, the method uses GA for tuning SVM parameters for the binary classifiers in decomposition strategy. There are three groups of experiments are performed, in the first one using the same values of parameters for all binary classifiers and in the second the GA generate values for every binary classifier in decomposition and the last one uses the default values of parameters as in LibSVM tool.
Error rate for testing or for validation is the measure, which riles on it to evaluate the performance. This measure can be describe as an indicator of the ability of the method in assign each element or instance to its valid category or class. Thus, the major goal is to minimize the error rate of testing when using the code matrix strategy for classifying multiclass datasets. Tables 11 and 12 show the comparisons in cases of using different values for SVM parameters in decomposition. The suggested approach minimizes error rate (ER.Rate.TS) with average 1.98, 5.37 and the standard deviation are 2.99, 6.21 for OAO and OAA methods respectfully, comparing to method developed by (Lorena and De Carvalho, 2008) (Samadzadegan et al., 2010) 70.00 93 86.00 9.82 0 50.042    This means that the performance of the method is satisfactory for code matrix strategy. Table 13 shows the comparisons with method developed by (Hsu and Lin, 2002b), which utilize some meta-heuristics approaches and grid search. The method uses only multiclass datasets with the suggested method. Statistical analysis on outcomes is performed using Wilcoxon test, Table 14 illustrates the produced results. Although, there are no significant differences found, but the proposed method win in achieving the better accuracy rate for all datasets from the with increasing rate 4.55% 4.30% and 4.14% from the Grid search, PSO and APS-SVM methods found in (Blondin and Saad, 2010). Thus, proposed method gives comparable outcomes than the method developed by (Blondin and Saad, 2010) as noted through the statistical results. From comparisons and statistical analysis of results in the previous tables, one may conclude that the obtained results by the method is very encouraged relatively to some other published methods. Moreover, the experimental results prove the proposed method is an effective approach for tuning SVM parameters in code matrix decomposition strategy form other methods, this may due to using SS to search for near optimal values of SVM parameters, where it is success to explore the all possible search space to extract and maintain the best values for parameters that enhance or improve the performance of SVMs classifiers in multiclass. This enhanced the overall performance of the method as shown in the previous sections. Also, the method can deal with high dimensional and large datasets, where the number of datasets that are used are nine datasets and the maximum number of classes is 11.

Results Analysis of Lung Cancer Diagnosis
The efficiency of the proposed method is validated in the previous section, where its successfully applied and experimented on nine datasets and produced promising results. As discussed throughout this paper, one major objective of this paper is the prediction of Lung cancer using the proposed method, which enhanced and validated for this purpose. Therefore; in this section, the method is applied for diagnosis of Lung cancer disease and its performance is investigated. The experimental datasets of lung cancer are obtained from UCI machine learning repository. The datasets contained 32 instances distributed into three classes which represent three types of lung cancer. There are 57 features (attributes) for each sample, where their values are arranged form 0 to 3. This datasets are mainly used for assistance of cancer diagnosis and to predict the cancer type.
For applying, the proposed method on Lung cancer datasets, some preprocessing procedures are applied which improve representation of this datasets for mining phase; i.e., prediction of cancer type using the parameter values generation of SVM. First, there are few missing values for some attributes; all missing attributes are replaced by the mean value of the corresponding attribute for all samples of the same class. After that, data normalization is required in order to prevent feature values in greater numeric ranges from dominating and to avoid numerical difficulties during the calculation. The normalization step is performed using Equation No. (12) as given below; where the data is normalized based on the minimum (Xmin) and maximum (Xmax) values of features: In addition, a new setting of the proposed method parameters is applied for this case study; where a new range from 0.1 to 10000 is specified and the maximum number of iterations was set to 75. The result is presented in Table 15 in terms of accuracy rate of training and testing datasets. As shown in this table, the highest accuracy is achieved and the best solution is reached in generation No. 82. The best solution is reached four times during the evaluation times of fitness Function (FT).
The produced results compared with other available results in the same field. Table 16 shows the comparisons of more than 16 methods proposed in literature as listed in (Polat and Güneş, 2008) and (Daliri, 2012). It's clear from comparisons that the proposed method achieves the highest accuracy rate and gives better results than the most comparable methods. It should be noted that there are some major differences with other approaches that proposed in literature; some methods perform feature reduction (Polat and Güneş, 2008) and (Daliri, 2012), where they reduced the features of the datasets into 4 and 8 features for the two methods respectively. As summarized in Table16; the achieved result of the proposed method was very promising and giving best performance relatively to related methods (Polat and Güneş, 2008;Daliri, 2012;Avci, 2012). Additionally, other related methods also produce notable results for data classification and diseases diagnosis (Afif et al., 2013;Afif and Hedar, 2012). Therefore, the optimized methods that based on meta-heuristic search approaches may be employed successfully to help doctors or medical specialists for diagnosis of lung cancer types and for other several diseases that should early be predicted to minimize their effect on patients.

Conclusion
This paper employed meta-heuristic approach called SS for tuning the SVM parameters values for each binary classifier involved in multiclass decompositions. The first experiments are conducted on 9 benchmark datasets that have a high dimensional and large size. The experimental results prove that the SS is practical for finding the best setting of SVM parameters, which enhance the SVM performance. Furthermore, the method is applied for lung cancer diagnosis as a real medical classification problem. The results demonstrate that the proposed method is promising and effective method for solving this multiclass problem and it can be extended in the future for other real problems. Moreover, the results are obtained using Gaussian kernel function; the method can also be investigated with other kernel functions. reviewed and prepared the final paper manuscript.
Mohammed H. Afif: Designed and developing the model, performed the experiments. He also prepared first draft of paper, reviewing and correction of the final paper manuscript.
Abdel-Rahman Hedar: Experimental design, contributed to results analysis.
Taysir H. Abdel Hamid: Reviewing and approved the paper.

Ethics
Authors confirm that this work is original research paper and no ethical issues behind the publication of this manuscript.