Incremental Learning and Evaluation of Structures of Rough Decision Tables

Abstract

AbstractRough decision tables were introduced by Pawlak in the context of rough set theory. A rough decision table represents, a non-functional in general, relationship between two groups of properties of objects, referred to as condition and decision attributes, respectively. In practical applications, the rough decision tables are normally learned from data. In this process, for better coverage of the domain of interest, they can be structured into hierarchies. To achieve convergence of the learned hierarchy of rough decision tables to a stable final state, it is desirable to avoid total regeneration of the learned structure after new objects, not represented in the hierarchy, are encountered. This can be accomplished through an incremental learning process in which the structure of rough decision tables is updated, rather than regenerated, after new observations appeared. The introduction and the investigation of this incremental learning process within the framework of the rough set model is the main theme of the article. The article is also concerned with evaluation of learned decision tables and their structures by introducing the absolute gain function to measure the quality of information represented by the tables.KeywordsBoundary AreaDecision TableIncremental LearningDecision AttributeInformation TableThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.