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International Journal of Machine Learning and Cybernetics : Volume 8, Issue 1, February 2017

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International Journal of Machine Learning and Cybernetics : Volume 1

Semi-supervised clustering for gene-expression data in multiobjective optimization framework

Content Provider	Springer Nature Link
Author	Alok, Abhay Kumar Saha, Sriparna Ekbal, Asif
Copyright Year	2015
Abstract	Studying the patterns hidden in gene expression data helps to understand the functionality of genes. But due to the large volume of genes and the complexity of biological networks it is difficult to study the resulting mass of data which often consists of millions of measurements. In order to reveal natural structures and to identify interesting patterns from the given gene expression data set, clustering techniques are applied. Semi-supervised classification is a new direction of machine learning. It requires huge unlabeled data and a few labeled data. Semi-supervised classification in general performs better than unsupervised classification. But to the best of our knowledge there are no works for solving gene expression data clustering problem using semi-supervised classification techniques. In the current paper we have made an attempt to solve the gene expression data clustering problem using a multiobjective optimization based semi-supervised classification technique with the aim to attain good quality partitions by using few labeled data. In order to generate the labeled data, initially Fuzzy C-means clustering technique is applied. In order to automatically determine the partitioning, multiple cluster centers corresponding to a cluster are encoded in the form of a string. In order to compute the quality of the obtained partitioning, values of five objective functions are computed. The effectiveness of this proposed semi-supervised clustering technique is demonstrated on five publicly available benchmark gene expression data sets. Comparison results with the existing techniques for gene expression data clustering prove that the proposed method is the most effective one. Statistical and biological significance tests have also been carried out.
Starting Page	421
Ending Page	439
Page Count	19
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	8
Issue Number	2
e-ISSN	1868808X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2015-02-15
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Gene expression data clustering Semi-supervised classification Multiobjective optimization Cluster validity index AMOSA Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Complex Systems Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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