Improved prediction accuracy with reduced feature set using novel binary gravitational search optimization

Sankhadip Saha, Dwaipayan Chakraborty

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

2 Scopus citations

Abstract

Improvement of classifier prediction accuracy is a long run burning issue over the years in the field of data mining and machine learning application. Optimized feature set is the best strategy and feature selection is the only key to the optimization problem. Various heuristic search algorithms are proposed in the literature for the feature set selection task. In this context we have enlightened the feature set exploration capacity of gravitational search algorithm (GSA) which is based on the Newton’s law of motion principle and the interaction of masses. Binary version of GSA with one modification is used for our application here. It is found that binary gravitational search algorithm (BGSA) is useful for finding only the relevant features while improving classifier accuracy from that with all features. We test our approach on six benchmark datasets from UCI machine learning repository.

Original languageEnglish (US)
Title of host publicationComputational Advancement in Communication Circuits and Systems - Proceedings of ICCACCS 2014
EditorsGoutam Kumar Dalapati, Moumita Mukherjee, Koushik Maharatna, P.K. Banerjee, Amiya Kumar Mallick, Amiya Kumar Mallick
PublisherSpringer Verlag
Pages177-183
Number of pages7
ISBN (Electronic)9788132222736
DOIs
StatePublished - 2015
Externally publishedYes
Event1st International Conference on Computational Advancement in Communication Circuits and Systems, ICCACCS 2014 - Kolkata, India
Duration: Oct 30 2014Nov 1 2014

Publication series

NameLecture Notes in Electrical Engineering
Volume335
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference1st International Conference on Computational Advancement in Communication Circuits and Systems, ICCACCS 2014
Country/TerritoryIndia
CityKolkata
Period10/30/1411/1/14

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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