IGLUE: An Instance-Based Learning System over Lattice Theory
نویسندگان
چکیده
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 diierent kind of technique based on Galois lattices or concept lattices. We present a new semi-lattice based system, IGLUE, that uses the entropy function with a top-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 Mahanalobis distance as a similarity measure between examples.
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