An affinity-based new local distance function and similarity measure for kNN algorithm

作者：Bhattacharya Gautam; Ghosh Koushik; Chowdhury Ananda S^*

来源：Pattern Recognition Letters, 2012, 33(3): 356-363.

DOI：10.1016/j.patrec.2011.10.021

• kNN
• Affinity function
• Similarity measure

In this paper, we propose a modified version of the k-nearest neighbor (kNN) algorithm. We first introduce a new affinity function for distance measure between a test point and a training point which is an approach based on local learning. A new similarity function using this affinity function is proposed next for the classification of the test patterns. The widely used convention of k, i.e., k = [root N] is employed, where N is the number of data used for training purpose. The proposed modified kNN algorithm is applied on fifteen numerical datasets from the UCI machine learning data repository. Both 5-fold and 10-fold cross-validations are used. The average classification accuracy, obtained from our method is found to exceed some well-known clustering algorithms.

• 出版日期2012-2-1