@ARTICLE { Rsds3197,
author = {Ohsuga, Setsuo and Dong, Ju-Zhen and Zhong, Ning}, 
title = {Rule discovery by soft induction techniques}, 
journal = {Neurocomputing: An International Journal}, 
number = 	{(no. 1-4)}, 
volume = 36, 
pages = {171-204}, 
publisher = {Elsevier Science Inc.}, 
address = {Amsterdam, The Netherlands}, 
month = {February}, 
year = 2001, 
url = {http://www.elsevier.com}, 
issn = {0925-2312}, 
abstract = {The paper describes two soft induction techniques, GDT-NR and GDT-RS, for discovering classification rules from databases with uncertainty and incompleteness. The techniques are based on a generalization distribution table (GDT), in which the probabilistic relationships between concepts and instances over discrete domains are represented. By using the GDT as a probabilistic search space, (1) unseen instances can be considered in the rule discovery process and the uncertainty of a rule, including its ability to predict unseen instances, can be explicitly represented in the strength of the rule; (2) biases can be flexibly selected for search control and background knowledge can be used as a bias to control the creation of a GDT and the rule discovery process. We describe that a GDT can be represented by a variant of connectionist networks (GDT-NR for short), and rules can be discovered by learning on the GDT-NR. Furthermore, we combine the GDT with the rough set methodology (GDT-RS for short). By using GDT-RS, a minimal set of rules with larger strengths can be acquired from databases with noisy, incomplete data. We compare GDT-NR with GDT-RS, and describe GDT-RS is a better way than GDT-NR for large, complex databases}, 
keywords = {inductive learning, knowledge discovery, generalization distribution table (GDT), rough sets, soft computing, uncertainty and incompleteness, background knowledge, hybrid system, }, 
}