A New Distance Metric Based on Class-Space Reduction
The ultimate goal of research regarding classification is to improve accuracy. Classification accuracy highly depends on overlapping areas among classes of the dataset. In general, a wider overlap area produces less classification accuracy. In this study, we suggest a new distance metric based on class-space reduction to improve classification accuracy. Proposed distance metric has same effect to rescale training/test data by moving data points in the direction of the center point of the class that the data points belong to. By conducting experiments using real datasets, we confirmed that many cases of new dataset generated by class-space reduction improved the classification accuracy for some classification algorithms.
KeywordsDistance metric Distance metric learning Classification Preprocessing Class-space reduction Bioinformatics
This study was supported by grant No. R31-2008-000-10069-0 from the World Class University (WCU) project of the Ministry of Education, Science & Technology (MEST) and the Korea Science and Engineering Foundation (KOSEF).
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