@INPROCEEDINGS { Rsds945,
author = {Cercone, Nick and Hu, Xiaohua Tony}, 
title = {Learning maximal generalized decision rules via discretization, generalization, and rough set feature selection}, 
booktitle = {Proceedings of the Ninth IEEE International Conference on Tools with Artificial Intelligence}, 
conference = {IEEE International Conference on Tools with Artificial Intelligence}, 
pages = {548-557}, 
publisher = {IEEE Computer Society Press}, 
address = {Los Alamitos, CA, USA}, 
month = {November}, 
year = 1997, 
issn = {1082-3409}, 
abstract = {We present a method to mine maximal generalized decision rules from databases by integrating discretization, generalization and rough sets feature selection. Our method reduces the data horizontally and vertically. In the first phase, discretization and generalization are integrated and the numeric attributes are discretized into a few intervals. Primitive values of symbolic attributes are replaced by high level concepts and some obvious superfluous or irrelevant symbolic attributes are also eliminated. Horizontal reduction is accomplished by merging identical tuples after the substitution of an attribute value by its higher level value in a predefined concept hierarchy for symbolic attributes or the discretization of continuous (or numeric) attributes. In the second phase, a novel context sensitive feature merit measure is used to rank the features, a subset of relevant attributes is chosen based on rough sets theory and the merit values of the features. A reduced table is obtained by removing those attributes which are not in the relevant attributes subset and the data set is further reduced vertically without destroying the interdependence relationships between the classes and the attributes. Rough sets based value reduction is further performed on the reduced table and all redundant condition values are dropped, finally, tuples in the reduced table are transformed into a set of maximal generalized decision rules. The experimental results on UCI data sets and an actual market database shows that our method can dramatically reduce the feature space and improve the learning accuracy.}, 
keywords = {UCI data sets, attribute value, context sensitive feature merit measure, deductive databases, discretization, feature space, generalization, high level concepts, identical tuples, learning accuracy, market database, maximal generalized decision rule learning, maximal generalized decision rule mining, maximal generalized decision rules, numeric attributes, predefined concept hierarchy, primitive values, redundant condition values, rough set feature selection, }, 
}