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Information 2018, 9(1), 6; doi:10.3390/info9010006

Gene Selection for Microarray Cancer Data Classification by a Novel Rule-Based Algorithm

Graduate School of Applied Informatics, University of Hyogo, Computational Science Center Building 5-7F, 7-1-28 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo 650-0047, Japan
Received: 31 October 2017 / Revised: 26 December 2017 / Accepted: 27 December 2017 / Published: 2 January 2018
(This article belongs to the Special Issue Feature Selection for High-Dimensional Data)
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Due to the disproportionate difference between the number of genes and samples, microarray data analysis is considered an extremely difficult task in sample classification. Feature selection mitigates this problem by removing irrelevant and redundant genes from data. In this paper, we propose a new methodology for feature selection that aims to detect relevant, non-redundant and interacting genes by analysing the feature value space instead of the feature space. Following this methodology, we also propose a new feature selection algorithm, namely Pavicd (Probabilistic Attribute-Value for Class Distinction). Experiments in fourteen microarray cancer datasets reveal that Pavicd obtains the best performance in terms of running time and classification accuracy when using Ripper-k and C4.5 as classifiers. When using SVM (Support Vector Machine), the Gbc (Genetic Bee Colony) wrapper algorithm gets the best results. However, Pavicd is significantly faster. View Full-Text
Keywords: gene selection; feature selection; microarray classification gene selection; feature selection; microarray classification

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Pino Angulo, A. Gene Selection for Microarray Cancer Data Classification by a Novel Rule-Based Algorithm. Information 2018, 9, 6.

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