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Influence of Metric on Classification Error of Distance-Based Classifiers
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SYSNO ASEP 0436236 Document Type V - Research Report R&D Document Type The record was not marked in the RIV Title Influence of Metric on Classification Error of Distance-Based Classifiers Author(s) Jiřina, Marcel (UIVT-O) SAI, RID Issue data Prague: ICS AS CR, 2014 Series Technical Report Series number V-1211 Number of pages 25 s. Language eng - English Country CZ - Czech Republic Keywords multidimensional data ; classifier ; distance ; metrics ; Hassanat metrics ; k-NN ; IINC Subject RIV BB - Applied Statistics, Operational Research Institutional support UIVT-O - RVO:67985807 Annotation Five types of classifiers that use sample distances for class estimation of an unknown sample was tested. Each classifier was tested with fifteen different metrics on 24 classification tasks from the UCI Machine Learning Repository. The metrics were compared and the best of them was found for each classifier. Surprisingly, the best metrics for all five types of classifiers is the Hassanat metrics. Classifiers were also compared and ranked according to their classification ability. Wilcoxon Test and Friedman Aligned test were used for statistical evaluation. Workplace Institute of Computer Science Contact Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Year of Publishing 2015
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