Arbiter Meta-Learning with Dynamic Selection of Multiple Classifiers
نویسندگان
چکیده
In data mining, the selection of appropriate classifier to estimate some unknown attribute of a new instance has an essential role for the quality of results. Recently interesting approaches with parallel and distributed computing have been presented. In this paper we discuss an approach that uses classifiers trained on a number of data subsets in parallel as in the arbiter meta-learning technique. We suggest collecting the performance information of base classifiers and arbiters and the use of this information during the application phase to select the appropriate classifier dynamically. Despite of many open questions we are convinced that the dynamic selection approach suggested in this paper includes interesting characteristics that at least in some situations offer benefits in comparison with other meta-learning
منابع مشابه
Arbiter Meta-Learning with Dynamic Selection of Classifiers and Its Experimental Investigation
In data mining, the selection of an appropriate classifier to estimate the value of an unknown attribute for a new instance has an essential impact to the quality of the classification result. Recently promising approaches using parallel and distributed computing have been presented. In this paper, we consider an approach that uses classifiers trained on a number of data subsets in parallel as ...
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