A feature ranking algorithm for problems with output of continuous range
作者： D. Tikk , T.D. Gedeon , K.W. Wong , S. Kovács
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摘要: This paper presents a feature ranking method adapted to fuzzy modelling with output from continuous range. Existing selection/ranking techniques are mostly suitable for classification problems, where the range of is discrete. These result in input (variables). Our approach exploits an arbitrary clustering model data. Using these clusters, similar methods can be used as classification, membership cluster (or class) will no longer crisp, but value determined by clustering. We propose application Sequential Backward Selection (SBS) search determine means different criterion functions. examined proposed and functions through comparative analysis.
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