@INPROCEEDINGS { Rsds3511,
author = {Busse-Grzymala, Jerzy W.}, 
title = {LEM2 With Interwal Extension: An Induction Algorithm for Numerical Attributes}, 
booktitle = {Proceedings of the Fourth International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
conference = {International Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery}, 
pages = {67--73}, 
publisher = {Japanese Research Group on Rough Sets}, 
address = {Tokyo, Japan}, 
month = {November}, 
year = 1996, 
editor = {Tsumoto, Shusaku and Kobayashi, Satoshi and Yokomori, Takashi and Tanaka, Hiroshi and Nakamura, Akira}, 
isbn = {4-947717-01-7}, 
abstract = {Induction of rules from data with numerical attributes is usually preceded by discretization. The process of discretization maps values of numerical attributes onto intervals (ranges of values). This paper presents a modification of the LEM2 algorithm, called the LEM2 Interval Extension Algorithm, where intervals in rules are extended during rule induction. The error rate of the rule sets produced by the LEM2 algorithm with Interval Extension is either smaller or dose not differ significantly from the error rate of the rule set produced by LEM2 without Interval Extension. However, LEM2 algorithm with Interval Extension induces smaller rule sets: fewer rules with fewer conditions. A smaller knowledge base requires less storage space and less computation time when classifying new examples. }, 
keywords = {rough set theory (RST), learning from examples, numerical attributes, discretization, interval extension, }, 
}