Weighted lambda precision models in rough set data analysis
نویسندگان
چکیده
We present a parameter free and monotonic alternative to the parametric variable precision model of rough set data analysis, based on the well known PRE index λ of Goodman and Kruskal. Using weighted (parametric) λ models we show how expert knowledge can be integrated without losing the monotonic property of the index. Based on a weighted λ index we present a polynomial algorithm to determine an approximately optimal set of predicting attributes. Finally, we exhibit a connection to Bayesian analysis.
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PRE and Variable Precision Models in Rough Set Data Analysis
We present a parameter free and monotonic alternative to the parametric variable precision model of rough set data analysis. The proposed model is based on the well known PRE index λ of Goodman and Kruskal. Using a weighted λ model it is possible to define a two dimensional space based on (Rough) sensitivity and (Rough) specificity, for which the monotonicity of sensitivity in a chain of sets i...
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