A novel Supervised Instance Selection algorithm
Shirish S. Sane, Ashok A. Ghatol · International Journal of Business Intelligence and Data Mining · 2007
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.