Generalized iterative RELIEF for supervised distance metric learning
Pattern Recognition
Orthogonal relief algorithm for feature selection
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Feature selection algorithm for data with both nominal and continuous features
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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RELIEF algorithm [4], [5] and its extensions [8], [9]are some of the most known filter methods for estimatingthe quality of attributes in classification problems dealingwith both dependent and independent features. Thesemethods attend to find all meaningful features for eachproblem (both weakly and strongly ones [6]) so they areusually employed like a first stage for detecting irrelevantattributes. Nevertheless, in this paper we checked thatRELIEF-family algorithms present some importantlimitations that could distort the selection of the finalfeatures' subset, specially in the presence of high-correlatedattributes. To overcome these difficulties, anew approach has been developed (WACSA algorithm),which performance and validity are verified on well-knowndata sets.