Hierarchical Rough Classifiers

Authors:
Sinh Hoa Nguyen;Hung Son Nguyen
Affiliations:
Polish-Japanese Institute of Information Technology, Koszykowa 86, 02008, Warszawa, Poland;Institute of Mathematics, Warsaw University, Banacha 2, 02-097 Warsaw, Poland
Venue:
RSEISP '07 Proceedings of the international conference on Rough Sets and Intelligent Systems Paradigms
Year:
2007

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Abstract

The major applications of rough set theory in data mining are related to the modeling of concepts using rough classifiers, i.e., the algorithms classifying unseen objects into lower or upper approximations of concepts. This paper investigates a class of compound classifiers called multi-level (or hierarchical) rough classifiers (MLRC). We present the most recent issues on the construction of such classifiers from data using concept ontology as an additional domain knowledge. The idea is based on the bottom-up manner to gradually synthesize the multi-layer rough classifier for the complex target concept from the simpler classifiers.We illustrate the proposed method by experiments on real-life data.