A novel Supervised Instance Selection algorithm

Authors:
Shirish S. Sane;Ashok A. Ghatol
Affiliations:
K.K.Wagh Institute of Engineering Education and Research, Nashik, Maharashtra, India.;Dr. Babasaheb Ambedkar Technological University, Lonere Tal, Mangaon Dist., Raigad, Maharashtra, India
Venue:
International Journal of Business Intelligence and Data Mining
Year:
2007

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Abstract

Instance selection is often used in case of lazy classifiers.This paper addresses the need of instance selection in case ofneural network and decision tree classifiers and presents a novelSupervised Instance Selection (SIS) algorithm. Initially, a neuralnetwork classifier is constructed using all training instances. Thealgorithm then selects a few instances using the certainty valuesof the wrapped neural network to construct a compact classifier.Empirical study made with standard datasets shows that SIS save on70% of storage space without degrading the accuracy. It isindependent of nature of the dataset and the tool used.