A rough set approach to reasoning about data
J.F. Peters, Andrzej Skowron · International Journal of Intelligent Systems · 2000
This issue of the International Journal of Intelligent Systems presents perspectives on a rough set approach to reasoning about data.Rules derived from decision tables instantiate a reasoning process for particular data sets, and reflect our evaluations of a data set.In this special issue, a number of foundation articles on the discovery and significance of decision rules are given.Underlying the study of the rough set approach to information systems is an interest in the discovery of effective means of approximating concepts reflected in data sets.A number of articles in this issue also pave the way toward what might be described as rough computation.This form of computing utilizes a rough set approach to reasoning about data in guiding the actions of agents and in facilitating communication between distributed agents.Conditional probabilities can be used to advantage in explaining conditions for decisions in decision rules.In this issue of IJIS, Pawlak presents an approach to exchanging mutual conditions and decisions in rules.Fundamental concepts concerning rules derived from decision tables as well as an approach to drawing conclusions from data are presented by Pawlak.''Inversed'' decision rules provide an explanation for decisions relative to conditions.Stefanowski and Vanderpooten introduce a procedure called Explore for extracting from data all decision rules that satisfy requirements.Explore is compared with the Grzymala-Busse algorithm LEM2, which is a rough set based rule induction approach to generating classification rules.Grzymala-Busse and Stefanowski introduce three discretization methods performed during rule induction.Rules induced by the new methods are shown to be simpler and stronger.Szczuka represents hyperplane-based decision rules in neural networks.In this approach to decision rules, an attribute-value space is partitioned into subsets bounded by hyperplanes.Classification of objects proceeds according to the position of rules relative to hyperplanes.Skowron and Stepaniuk have shown how information granules can be defined by sets of decision rules.Granules defined by rules are examples of sequences of granules.Rule-based information granules provide a basis for reasoning in a distributed environment.Agents in such an environment Ž .can be designed so that concepts from a source server agent can be approximated by a receiving agent using a rough set approach in constructing information granules.In the paper by Yao, the focus is on information granulation and Ž .