Dimensionality reduction for efficient classification of DNA repair genes

A. Vidya*, V. Manohar, V. P. Shwetha, K. R. Venugopal, L. M. Patnaik

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

DNA damage is an imperative process which plays a crucial role in ageing demanding the need for classification of DNA repair genes into ageing and non-ageing. In our paper, we employ a data mining approach for classifying DNA repair genes using their various characteristic features. The classification models built were difficult to analyze and interpret due to the curse of dimensionality present in the gene dataset. This difficulty is overcome by adopting Dimensionality Reduction which is a well-known pre-processing technique. The Feature subset selection technique along with various search methods is used to reduce the dataset without affecting the integrity of the original dataset. The reduction in the dataset enabled the use of Multilayer perceptron in the efficient analysis of the dataset. The classifiers showed better performance on the reduced dataset when compared to the original dataset.

Original languageEnglish
Title of host publicationWireless Networks and Computational Intelligence - 6th International Conference on Information Processing, ICIP 2012, Proceedings
Pages536-545
Number of pages10
DOIs
Publication statusPublished - 2012
Event6th International Conference on Information Processing, ICIP 2012 - Bangalore, India
Duration: 10-08-201212-08-2012

Publication series

NameCommunications in Computer and Information Science
Volume292 CCIS
ISSN (Print)1865-0929

Conference

Conference6th International Conference on Information Processing, ICIP 2012
Country/TerritoryIndia
CityBangalore
Period10-08-1212-08-12

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics

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