A hybrid feature selection method based on Binary Jaya algorithm for micro-array data classification

Abstract

Micro-array technology generates high-dimensional data. The high dimensionality of data hampers the learning capability of machine learning algorithms. Dimensionality can be reduced using feature selection (FS) techniques, which is an important and essential pre-processing step to process high dimensional data. In this work, a hybrid filter–wrapper approach is proposed for feature selection. The multi-attribute decision-making method called Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used as a filter for informative feature extraction. Further, Binary Jaya algorithm with time-varying transfer function is proposed as a wrapper feature selector to find the optimal subset of features. The proposed approach is tested on 10 benchmark micro-array datasets and compared with state-of-the-art methods. Experimental results suggest that the proposed approach performs better in terms of classification accuracy and it is 10 times faster than existing approaches.