Evolutionary Optimization on k-Nearest Neighbors Classifier for Imbalanced Datasets

Classification is a significant task in machine learning. Common classification algorithms include decision tree, k-nearest neighbors (kNN), support vector machine, and so on. The kNN algorithm is a simple yet useful classification tool in practice. However, its performance may be deeply affected by the properties of dataset, such as imbalance of classes and noisy features. To deal with these problems and enhance the performance of classification, this study proposes optimizing the distance function and class-voting weights of kNN by genetic algorithm. The proposed method is called evolutionary optimized feature and class weights kNN (FCWkNN). The performance of FCWkNN is examined on different datasets. Experimental results show that the combined optimization of feature weights and class weights is capable of improving kNN on imbalanced datasets.