Abstract

. In the context of machine learning from examples this paper deals with the problem of estimating the quality of attributes with and without dependencies among them. Kira and Rendell (1992a,b) developed an algorithm called RELIEF, which was shown to be very efficient in estimating attributes. Original RELIEF can deal with discrete and continuous attributes and is limited to only two-class problems. In this paper RELIEF is analysed and extended to deal with noisy, incomplete, and multi-class data sets. The extensions are verified on various artificial and one well known real-world problem. 1 Introduction This paper deals with the problem of estimating the quality of attributes with strong dependencies to other attributes which seems to be the key issue of machine learning in general. Namely, for particular problems (e.q. parity problems of higher degrees) the discovering of dependencies between attributes may be unfeasible due to combinatorial explosion. In such cases efficient heuris...

Citations

3199	Induction of decision trees - Quinlan - 1986
325	A practical approach to feature selection - Kira, Rendell - 1992
239	feature selection problem: Traditional methods and a new algorithms”, Proceedings of ninth national conference on artificial intelligence - Kira, Rendell - 1992
113	P.L.Stone, Experiments in Induction - Hunt - 1967
1	Roskar E.: Experiments in inductive learning of medical diagnostic rules - Kononenko, Bratko - 1984
1	R.M.: Rule induction using information theory. In. G.Piatetsky-Shapiro & W.Frawley (eds.): Knowledge Discovery in Databases - Smyth - 1990