A Study Of Instance Selection Algorithm Based On Euclidean Distance

The basic nearest neighbor classifiers suffer from the common problem that the instances used to train the classifier are all stored indiscriminately, and as a result, the required memory storage is huge and response time becomes slow. In this paper, a new Instances Selection algorithm based on Classification Contribution Function shortly named ISCC and IISDC are presented. Meanwhile,two functions are introduced to evaluate the classification ability of the instances. Then an instance with the highest value of Classification Contribution Function is added to the condensed subset. The time complexity of ISCC and IISDC are O(n~2). Compared to traditional methods, such as FCNN,ICF and ENN, the condensed sets obtained by ISCC and IISDC are superior in storage and classification accuracy.