2006/09/03 by Vladislav Gennadievich Malyshkin, Vladislav Malyshkin, Ray Bakhramov +4
Computer Science · #Data Mining Algorithms and Applications #Machine Learning and Data Classification #Rough Sets and Fuzzy Logic #cs.LG
paper · pdf · doi:10.48550/arxiv.cs/0609007
24 pages
arxiv created 2006/09/03 · arxiv updated 2009/12/01
An approach to the classification problem of machine learning, based on building local classification rules, is developed. The local rules are considered as projections of the global classification rules to the event we want to classify. A massive global optimization algorithm is used for optimization of quality criterion. The algorithm, which has polynomial complexity in typical case, is used to find all high--quality local rules. The other distinctive feature of the algorithm is the integration of attributes levels selection (for ordered attributes) with rules searching and original conflicting rules resolution strategy. The algorithm is practical; it was tested on a number of data sets from UCI repository, and a comparison with the other predicting techniques is presented.