Dimensionality Invariant Similarity Measure

This paper presents a new similarity measure to be used for general tasks including supervised learnin g, which is represented by the K-nearest neighbor clas sifier (KNN). The proposed similarity measure is in variant to large differences in some dimensions in the feature space. The proposed metric is proved mathematicall y to be a metric. To test its viability for different applica tions, the KNN used the proposed metric for classif ying test examples chosen from a number of real datasets. Compared to some other well known metrics, the experimental results show that the proposed metric is a promisin g distance measure for the KNN classifier with stro ng potential for a wide range of applications. (Hassanat B. A. Dimensionality Invariant Similarity Measure. J Am Sci 2014;10(8):221-226). (ISSN: 1545- 1003). http://www.jofamericanscience.org . 31

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