An Incremental Learning Algorithm Based on Rough Set Theory

Authors:
Yinghong Ma;Yehong Han
Affiliations:
School of Management, Shandong Normal University, Jinan, 250014, P.R. China;School of Management, Shandong Normal University, Jinan, 250014, P.R. China
Venue:
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Year:
2007

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Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Rough sets and intelligent data analysis

Information Sciences—Informatics and Computer Science: An International Journal

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Abstract

A decision table is a pair S = (U,A), where U and A are nonempty finite sets called the universe and primitive attributes respectively. In order to computing the minimal rule sets on the decision table in which a new instance is added, a classification of the new instances and a criteria for the minimum recalculation are given, an incremental learning algorithm is presented and this algorithm is proved that can be used to the consistent and the inconsistent decision tables. The complexity of this algorithm is also obtained.