Title: A novel Supervised Instance Selection algorithm
Authors: Shirish S. Sane, Ashok A. Ghatol
Addresses: K.K.Wagh Institute of Engineering Education and Research, Nashik, Maharashtra, India. ' Dr. Babasaheb Ambedkar Technological University, Lonere Tal, Mangaon Dist., Raigad, Maharashtra, India
Abstract: Instance selection is often used in case of lazy classifiers. This paper addresses the need of instance selection in case of neural network and decision tree classifiers and presents a novel Supervised Instance Selection (SIS) algorithm. Initially, a neural network classifier is constructed using all training instances. The algorithm then selects a few instances using the certainty values of the wrapped neural network to construct a compact classifier. Empirical study made with standard datasets shows that SIS save on 70% of storage space without degrading the accuracy. It is independent of nature of the dataset and the tool used.
Keywords: classification; data mining; data reduction; instance selection; neural networks; decision trees.
DOI: 10.1504/IJBIDM.2007.016384
International Journal of Business Intelligence and Data Mining, 2007 Vol.2 No.4, pp.471 - 495
Published online: 23 Dec 2007 *
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