Classificatory filtering in decision systems

Autor(en): Wang, H
Duntsch, I
Gediga, G
Stichwörter: artificial intelligence; Computer Science; Computer Science, Artificial Intelligence; data filtering; data reduction; decision system; lattice; machine learning; ROUGH; rough set
Erscheinungsdatum: 2000
Herausgeber: ELSEVIER SCIENCE INC
Journal: INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
Volumen: 23
Ausgabe: 2
Startseite: 111
Seitenende: 136
Zusammenfassung: 
Classificatory data filtering is concerned with reducing data in size while preserving classification information. Duntsch and Gediga [I. Duntsch, G. Gediga, International Journal of Approximate Reasoning 18 (1998) 93-106] presented a first approach to this problem. Their technique collects values of a single feature into a single value. In this paper we present a novel approach to classificatory filtering, which can be regarded as a generalisation of the approach in the above mentioned paper. This approach is aimed at collecting values of a set of features into a single value. We look at the problem abstractly in the context of lattices. We focus on hypergranules (arrays of sets) in a problem domain, and it turns out the collection of all hypergranules can be made into a lattice. Our solution (namely, LM algorithm) is formulated to find a set of maximal elements for each class, which covers all elements in a given dataset and is consistent with the dataset. This is done through the lattice sum operation. In terms of decision systems, LM collects attributes values while preserving classification structure. To use the filtered data for classification, we present and justify two measures (C-0 and C-1) for the relationship between two hypergranules. Based on the measures, we propose an algorithm (C2) for classification. Both algorithms are evaluated using real world datasets and are compared with C4.5. The result is analysed using statistical test methods and it turns out that there is no statistical difference between the two. Regression analysis shows that the reduction ratio is a strong indicator of prediction success. (C) 2000 Elsevier Science Inc. All rights reserved.
ISSN: 0888613X
DOI: 10.1016/S0888-613X(99)00040-7

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