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Finding Accuracy in Feature Selection Using Firefly Algorithm with Rough Set theory

Feature selection techniques play a vital role in bioinformatics applications. In addition to the large group of techniques that have already been developed in the machine learning and data mining fields, specific applications in bioinformatics have led to possess of newly proposed techniques. In this paper, a method for feature selection is based on Firefly Optimization (FFO) with Rough Set Theory(RST) is proposed. Data sets include a large volume of features with irrelevant and redundant features. Redundant and irrelevant features reduce accuracy. The main aim of this paper is to select a subset of relevant features. A statistical metric-based feature selection technique has been proposed in order to reduce the size of the extracted feature vector. The proposed method shows the improvement significantly in terms of performance measure metrics: accuracy, sensitivity, specificity, computation time and so on. FFO technique is applied to determine the features globally according to the light intensity. Then the selected features are grouped together to make a subset and applied RST to find the optimized feature. This optimized feature is used to analyze the protein information in the organisms and improve the feature selection accuracy and reduce the computation time in protein data analysis.