A novel approach for dimension reduction of microarray. (December 2017)

• Record Type: Journal Article
• Authors: Aziz, Rabia; Verma, C.K.; Srivastava, Namita
• Is Part Of: Computational biology and chemistry. Volume 71(2017)
• Page Start: 161
• Page End: 169
• Publication Date: 2017-12
• Subjects: Feature selection (FS) -- Artificial bee colony (ABC) -- Independent component analysis (ICA) -- Naïve bayes (NB) -- Cancer classification; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.10.009
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: This paper present a novel dimension reduction method which improves the performance of NB classifier for microarray data. Proposed approach optimize ICA feature vectors by using ABC algorithm with NB classifier for finding best subset of genes. Experimental result shows that proposed approach gives better result as compared to other exiting methods for NB classifier. Abstract: This paper proposes a new hybrid search technique for feature (gene) selection (FS) using Independent component analysis (ICA) and Artificial Bee Colony (ABC) called ICA + ABC, to select informative genes based on a Naïve Bayes (NB) algorithm. An important trait of this technique is the optimization of ICA feature vector using ABC. ICA + ABC is a hybrid search algorithm that combines the benefits of extraction approach, to reduce the size of data and wrapper approach, to optimize the reduced feature vectors. This hybrid search technique is facilitated by evaluating the performance of ICA + ABC on six standard gene expression datasets of classification. Extensive experiments were conducted to compare the performance of ICA + ABC with the results obtained from recently published Minimum Redundancy Maximum Relevance (mRMR) +ABC algorithm for NB classifier. Also to check the performance that how ICA + ABC works as feature selection with NB classifier, compared the combination of ICA with popular filter techniques and with other similar bio inspired algorithm such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The result shows that ICA + ABC has a significant ability to generate small subsets of genes from the ICA feature vector, that significantly improve the classification accuracy of NB classifier compared to other previously suggested methods.