Title :
Weighted λ precision models in rough set data analysis
Author :
Düntsch, Ivo ; Gediga, Günther
Author_Institution :
Dept. of Comput. Sci., Brock Univ., St. Catharines, ON, Canada
Abstract :
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.
Keywords :
Bayes methods; data analysis; polynomial approximation; rough set theory; Bayesian analysis; PRE index λ; approximate optimal set; attributes prediction; expert knowledge; monotonic alternative; parameter free; parametric variable precision model; polynomial algorithm; rough set data analysis; weighted λ precision models; Analytical models; Approximation methods; Data analysis; Data models; Indexes; Predictive models;
Conference_Titel :
Computer Science and Information Systems (FedCSIS), 2012 Federated Conference on
Conference_Location :
Wroclaw
Print_ISBN :
978-1-4673-0708-6
Electronic_ISBN :
978-83-60810-51-4
