TY - GEN
T1 - Improved prediction accuracy with reduced feature set using novel binary gravitational search optimization
AU - Saha, Sankhadip
AU - Chakraborty, Dwaipayan
N1 - Publisher Copyright:
© Springer India 2015.
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84925357091&partnerID=8YFLogxK
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U2 - 10.1007/978-81-322-2274-3_22
DO - 10.1007/978-81-322-2274-3_22
M3 - Conference contribution
AN - SCOPUS:84925357091
T3 - Lecture Notes in Electrical Engineering
SP - 177
EP - 183
BT - Computational Advancement in Communication Circuits and Systems - Proceedings of ICCACCS 2014
A2 - Dalapati, Goutam Kumar
A2 - Mukherjee, Moumita
A2 - Maharatna, Koushik
A2 - Banerjee, P.K.
A2 - Mallick, Amiya Kumar
A2 - Mallick, Amiya Kumar
PB - Springer Verlag
T2 - 1st International Conference on Computational Advancement in Communication Circuits and Systems, ICCACCS 2014
Y2 - 30 October 2014 through 1 November 2014
ER -