Title :
Classification and rule induction based on rough sets
Author :
Gryzmala-Busse, J.W. ; Wang, Chien Pei B
Author_Institution :
Dept. of Electr. & Comput. Eng., Kansas Univ., Lawrence, KS, USA
Abstract :
Rules induced by machine learning systems from training data may be used for classification of new cases. The main objective of this paper is optimization of classification of unseen cases. In the experiments described in the paper, rules were induced by the system LERS (Learning from Examples based an Rough Sets). The classification system of LERS uses four parameters: strength-factor, specificity-factor, matching-factor and support. The paper shows the best choice of those four parameters in terms of error rate
Keywords :
fuzzy set theory; learning by example; pattern classification; LERS; machine learning systems; matching-factor; rough sets; rule induction; specificity-factor; strength-factor; support; training data; Diseases; Error analysis; Government; Hospitals; Knowledge acquisition; Learning systems; Machine learning algorithms; Rough sets; Testing; Training data;
Conference_Titel :
Fuzzy Systems, 1996., Proceedings of the Fifth IEEE International Conference on
Conference_Location :
New Orleans, LA
Print_ISBN :
0-7803-3645-3
DOI :
10.1109/FUZZY.1996.552273
