A Survey of k Nearest Neighbor Algorithms for Solving the Class Imbalanced Problem

Abstract

$k$ nearest neighbor ( $k$ NN) is a simple and widely used classifier; it can achieve comparable performance with more complex classifiers including decision tree and artificial neural network. Therefore, $k$ NN has been listed as one of the top 10 algorithms in machine learning and data mining. On the other hand, in many classification problems, such as medical diagnosis and intrusion detection, the collected training sets are usually class imbalanced. In class imbalanced data, although positive examples are heavily outnumbered by negative ones, positive examples usually carry more meaningful information and are more important than negative examples. Similar to other classical classifiers, $k$ NN is also proposed under the assumption that the training set has approximately balanced class distribution, leading to its unsatisfactory performance on imbalanced data. In addition, under a class imbalanced scenario, the global resampling strategies that are suitable to decision tree and artificial neural network often do not work well for $k$ NN, which is a local information-oriented classifier. To solve this problem, researchers have conducted many works for $k$ NN over the past decade. This paper presents a comprehensive survey of these works according to their different perspectives and analyzes and compares their characteristics. At last, several future directions are pointed out.