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Tuning Suitable Features Selection using Mixed Waste Classification Accuracy

Solid waste is inextricably related to industrial growth and urbanization. Countries’ economic prosperity rises as they urbanize. Consumption of goods and services rises in tandem with the levels of life and disposable incomes, resulting in an increase in waste generation. According to Karak et al. [1], nearly 1.3 billion tons of MSW are produced globally per year, or 1.2 kg each capita per day. The real per capita figures, on the other hand, are extremely volatile, as waste generation rates vary greatly across nations, cities, and also within cities. Solid waste is traditionally thought of as a ‘urban’ epidemic. Rural people, on average, are wealthier, consume less store-bought goods, and reuse and recycle more. As a result, waste production rates in rural areas are much smaller. More than half of the world’s population now lives in cities, and the pace of urbanization is rapidly growing. By 2050 [2], as many people will live in cities as the population of the whole world in 2000. This will add challenges to waste disposal. People and companies would almost definitely have to take on greater responsibility for waste generation and disposal, especially in terms of product design and waste separation. A stronger focus on ‘urban extraction’ is also expected to arise, as cities are the largest supplier of resources like metal and paper. Tons of waste are created every day, creating a major problem for various cities and municipal authorities due to a lack of landfill capacity to dispose of such waste. Toxic hazard materials in the waste cause health concerns as well as environmental harm. Due to a lack of landfill space and environmental pollution, as well as its economic effects, recycling has become a major problem [3].


INTRODUCTION
Solid waste is inextricably related to industrial growth and urbanization. Countries' economic prosperity rises as they urbanize. Consumption of goods and services rises in tandem with the levels of life and disposable incomes, resulting in an increase in waste generation. According to Karak et al. [1], nearly 1.3 billion tons of MSW are produced globally per year, or 1.2 kg each capita per day. The real per capita figures, on the other hand, are extremely volatile, as waste generation rates vary greatly across nations, cities, and also within cities. Solid waste is traditionally thought of as a 'urban' epidemic. Rural people, on average, are wealthier, consume less store-bought goods, and reuse and recycle more. As a result, waste production rates in rural areas are much smaller. More than half of the world's population now lives in cities, and the pace of urbanization is rapidly growing. By 2050 [2], as many people will live in cities as the population of the whole world in 2000. This will add challenges to waste disposal. People and companies would almost definitely have to take on greater responsibility for waste generation and disposal, especially in terms of product design and waste separation. A stronger focus on 'urban extraction' is also expected to arise, as cities are the largest supplier of resources like metal and paper. Tons of waste are created every day, creating a major problem for various cities and municipal authorities due to a lack of landfill capacity to dispose of such waste. Toxic hazard materials in the waste cause health concerns as well as environmental harm. Due to a lack of landfill space and environmental pollution, as well as its economic effects, recycling has become a major problem [3].
The methods from neural networks [4] and other classification methods [5], tree classifier, and quadratic discriminant analysis [3], with optical sensors and controller [6], with image processing software capability [7,8] can provide a promising results for paper and plastic sorting for waste management using artificial intelligence. Hence, for many artificial intelligence methods, the optimized features need to be identified, to ensure correct classification of the mixed waste.
Many interests among researchers published on classification accuracy which discussed on best feature selection [9][10][11]. Still, to some researchers, the focus also arises on classification accuracy and computational time [12,13]. However, the scope of this work is to show how the reduction of features can contribute to higher classification accuracy of mixed waste classification.

Dataset and Training
In this study, 320 sample images were taken for seven different dry waste garbage classes. The features data was extracted from greyscale, segmented greyscale images and binarized image of the captured RGB images using Matlab2019b. Table 1 dataset used for training in each class.

Features extraction
From the observation of mixed waste classes which consist of crumble (paper/plastic), flat (paper/plastic), tin can, bottle (plastic/ glass), cup (paper/plastic), plastic box and paper box, the following 40 features have been selected for the class of interest as shown in Table 2 which extracted from Figure 1 via image processing method. Table 3 shows the types of classifiers used with respect to their specifications. Figure 4 shows the block diagram for training of the developed system.

Features Selections
There are several stages of feature selections carried out from this study. The first stage, all 40 features are used. However, accuracy of the 40 features during training was less than 77.8%. To address the shortcoming, several tests were conducted and number of redundant features which contribute less and reduced the percentage rate of the training were removed from the list.   where o is the total number of elements and y is the corresponding number of elements of array a, z is the corresponding elements of array b. Array a is the first half of the array C, and array b is the second half of the array C.
where C is the white pixel summation array, E is the expected value of quantity, m is the mean of C, s is the standard deviation of C. where C is the white pixel array, E is the expected value of quantity, m is the mean of C, s is the standard deviation of C.
where C is the white pixel array, N is the observations/values in the array.
where C is metric of the white pixel summation of the image with N number of values greater than 0, and m is the mean of C.
where n is the total numbers of absolute value array, m is the mean and s is the standard deviation of the C.
(Continued) In the second stage, 20 features were selected from the 40 features. After many features tuning and combination, it was observed that F1-F5, F7, F8, F10, F14, F30-F35, F37-F40 were insignificant and did not contribute to the classification process. These features were removed which then contributed to mean of 79.12% In the third stage, only 17 features were used which consists of F9, F11-F13, F15-F24, F26-F28. For these 17 features, Q.SVM and C.SVM showed accuracy above 88%.
where C is metric of the white pixel summations of the image with N number of values greater than 0, and m is the mean of C.

F11: Standard deviation
Greyscale of segmented sample image ( Figure 3) 1 where C is metric of the greyscale of the segmented image with N number of values greater than 0, and m is the mean of C.

Classifiers Training Results
The training accuracy for respective classifiers with respect to 40, 20, 17 and 15 features are shown in Table 4. For Q.SVM, C.SVM, Fine KNN, E. Boosted Trees and E. Bagged Trees have consistent higher accuracy for 17 features. The results verified that the 17 features gave good characteristic of the waste, therefore proves that good features will always provide higher accuracy. The best performance classifier with 17 features was Q.SVM which has accuracy of 90.69%.

Classification Results of Mixed Waste Classification
Based on the training performance which discussed in Section 3.2, Q.SVM has the highest accuracy of the optimized 17 features. With that confirmation, testing was done using 320 images of mixed waste which consist of crumble (paper/plastic), flat (paper/plastic), tin can, bottle (plastic/glass), cup (paper/plastic), plastic box and paper box. Table 5 shows the summary of the testing accuracy In the fourth stage, F9 and F11 were removed. F12, F13, F15-F24, F26-F28 were maintained. However, the accuracy was severely impacted with average results lest than 80%. Figure 5 summed up the performance with respect to features selections stages.

CONCLUSION
From this study, it was observed that relevant features and accuracy are good matched to ensure the performance of mixed waste classification. The relevant features were studied stage by stage. In the first stage, even though more features were assigned, the classifiers training showed poor performance. In the second stage, after reduction of features, by experimenting the features and training rate, the redundant features which contributed to low accuracy have been removed. Eventually the classification rate of training increased to average of 79.12%. Further reduction to 17 features, have contributed to higher average accuracy of 86.22%. However, further reduction did not contribute to better classification rate.
The selected 17 features with Q.SVM as classifier also proved with higher accuracy for training with 90.69% and testing with 89.9%. In a nutshell, the important of the correct features selection is crucial for mix waste classification. Optimal features selection, proved for higher accuracy of mixed waste classification. On contrary, more features not necessarily contributed to higher accuracy.

Dr. Norrima Mokhtar
She received the B. Eng. degree from University of Malaya, the M. Eng. and the PhD degree from Oita Univerity, Japan. She is currently a senior lecturer in the Department of Electrical Engineering, University of Malaya. Her research interests are signal processing and human machine interface.

Dr. Wan Amirul Bin Wan Mohd Mahiyidin
He received the M. Eng. degree from the Imperial College London in 2009, the M.Sc. degree from University of Malaya in 2012, and the PhD degree from University of Canterbury in 2016. He is currently a senior lecturer at the Department of Electrical Engineering, University of Malaya. His research interests are multiple antennas system, cooperative MIMO, channel modelling and positioning system.

Dr. Noraisyah Binti Mohamed Shah
She received the B. Eng. degree from University of Malaya in 1999, Master Degree from Oita Universitym, Japan, and the PhD degree from George Mason University. She is currently a senior lecturer at the Department of Electrical Engineering, University of Malaya. Her research interests are image processing, machine learning and satellite communication.

Prof. Dr. Raveendran Paramesran
He (SM'01) received the B.Sc. and M.Sc. degrees in electrical engineering from South Dakota State University, Brookings, SD, USA, in 1984 and 1985, respectively, and the PhD degree from the University of Tokushima, Japan, in 1994. He was a Systems Designer with Daktronics, USA. He joined the Department of Electrical Engineering, University of Malaya, Kuala Lumpur, in 1986, as a Lecturer. In 1992, he received a Ronpaku Scholarship from Japan. He was promoted to Associate Professor in 1995, then was promoted to Professor in 2003. His research areas include image and video analysis, formulation of new image descriptors for image analysis, fast computation of orthogonal moments, analysis of EEG signals, and data modeling of substance concentration acquired from non-invasive methods. His contributions can be seen in the form of journal publications, conference proceedings, chapters in books and an international patent to predict blood glucose levels using non-parametric model. He has successfully supervised to completion 14 PhD students and 12 students in M. Eng. Sc. (Masters by research). He is currently a member of the Signal Processing Society.