Title: Adaptive improved binary PSO-based learnable Bayesian classifier for dimensionality reduced microarray data
Authors: Sahu Barnali; Dehuri Satchidananda; Jagadev Alok Kumar
Addresses: Department of Computer Science and Engineering, Siksha 'O' Anusandhan University, Bhubaneswar, 751030, Odisha, India ' Department of Information and Communication Technology, Fakir Mohan University, Vyasa Vihar, Balasore, 756019, Odisha, India ' School of Computer Engineering, KIIT University, Bhubaneswar, 751024, Odisha, India
Abstract: This article presents, an adaptive improved binary particle swarm optimisation-based learnable Bayesian classifier for dimensionally reduced microarray data. In the first fold of this two-folded work, the problem of dimension has been reduced by unsupervised method of feature reduction. The k-means clustering algorithm has been applied on the microarray data to group functionally redundant genes followed by application of signal-to-noise-ratio ranking technique to generate an intermediate feature subset consisting of most relevant and non-redundant feature subsets. In the second fold, the feature subset has been given to an adaptive binary particle swarm optimisation-based learnable Bayesian classifier for simultaneous selection of features and classification. We have conducted an extensive experimental work on a few benchmark datasets to validate its classification accuracy with and without reducing the dimensionality of microarray data. It was observed that our method is not only accepted as a good classifier over methods, which are considered here for comparison but also be treated as an alternative method of reducing dimension of the problem.
Keywords: microarray data; feature selection; signal-to-noise-ratio; classification; Bayesian classifier; adaptive PSO.
International Journal of Medical Engineering and Informatics, 2019 Vol.11 No.3, pp.265 - 285
Available online: 19 Aug 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article