Feature Selection Algorithm Using Effective Range and Overlapping Area for Classification of Gene Expression Data
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Date
2019
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Department of Computer Science & Engineering, NITS
Abstract
Feature selection is an important issue in the field of machine learning and data mining. Classifier’s accuracies and other generalization performance depend on the outcome of the feature selection. Feature selection can help in building a simple model with high accuracy to solve a complex real-world problem. Prediction and diagnosis of disease at an early stage can help many people to get rid of that disease. Classification of gene expression data can play a major role in the prediction of disease. Recently statistically defined feature selection algorithm named Effective Range based Gene Selection (ERGS) and Improved Feature Selection based on Effective Range (IFSER) is proposed. Where, ERGS considers only the overlapping area (OA) of the effective range of each class for every feature, on the other hand, IFSER also uses the other relationship (inclusion relation) of the effective range of each class. But both ERGS and IFSER fails to handle the one situation when the effective range of one class is completely included in the effective range of the other class. And the overlapping area and number of samples in the overlapping area and including area is incorrectly measures by both the algorithms. In this thesis, we are presenting a modified algorithm for feature selection which will overcome the limitation of ERGS and IFSER algorithms. To showcase the effectiveness of our proposed method several experiments on different gene expression datasets are performed and results are compared with other well-known feature selection algorithms. Experimental results manifest that the proposed modification in ERGS and IFSER improves the classification accuracies on gene expression dataset.