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

• Published in: Computers & electrical engineering Vol. 90; p. 106963
• Main Authors: Chaudhuri, Abhilasha, Sahu, Tirath Prasad
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.03.2021; Elsevier BV
• Subjects: Algorithms; Arrays; Classification; Decision making; Dimensionality reduction; Feature extraction; Feature selection; Hybrid feature selection; Jaya algorithm; Machine learning; Micro-array data; TOPSIS; Transfer functions; Dimensionality reduction; Micro-array data; Jaya algorithm; Feature selection; TOPSIS; Hybrid feature selection
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2020.106963

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. [Display omitted] •Filter–wrapper based a new hybrid feature selection method is proposed.•TOPSIS is used as filter method to select most informative features.•Binary Jaya algorithm with time varying transfer function is proposed as wrapper.•Proposed approach is evaluated on ten benchmark datasets.•The proposed approach is ten times faster than its competitors.