IGLUE: an instance-based learning system over lattice theory

Author

Njiwoua, Patrick ; Nguifo, Engelbert Mephu

Author_Institution

Artois Univ., Lens, France

fYear

1997

Firstpage

75

Lastpage

76

Abstract

Concept learning is one of the most studied areas in machine learning. A lot of work in this domain deals with decision trees. In this paper, we are concerned with a different kind of technique based on Galois lattices or concept lattices. We present a new semilattice based system, IGLUE, that uses the entropy function with a tap-down approach to select concepts during the lattice construction. Then IGLUE generates new relevant numerical features by transforming initial boolean features over these concepts. IGLUE uses the new features to redescribe examples. Finally, IGLUE applies the Mahanalobe´s distance as a similarity measure between examples

Keywords

decision theory; knowledge based systems; learning (artificial intelligence); Galois lattices; IGLUE; Mahanalobe´s distance; concept lattices; concept learning; decision trees; entropy function; initial boolean features; instance-based learning system; lattice theory; machine learning; semilattice based system; similarity measure; tap-down approach; Entropy; Lattices; Law; Learning systems; Legal factors; Lenses; Nearest neighbor searches; Neural networks; Taxonomy; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Tools with Artificial Intelligence, 1997. Proceedings., Ninth IEEE International Conference on

Conference_Location

Newport Beach, CA

ISSN

1082-3409

Print_ISBN

0-8186-8203-5

Type

conf

DOI

10.1109/TAI.1997.632239

Filename

632239