Combining Local and Global KNN With Cotraining

Semi-supervised learning is a machine learning paradigm in which the induced hypothesis is improved by taking advantage of unlabeled data. It is particularly useful when labeled data is scarce. Cotraining is a widely adopted semi-supervised approach that assumes availability of two views of the training data a restrictive assumption for most real world tasks. In this paper, we propose a one-view Cotraining approach that combines two different k-Nearest Neighbors (KNN) strategies referred to as global and local k-NN. In global KNN, the nearest neighbors selected to classify a new instance are given by the training examples which include this instance as one of their own k-nearest neighbors. In local KNN, on the other hand, the neighborhood considered when classifying a new instance is computed with the traditional KNN approach. We carried out experiments showing that a combination of these strategies significantly improves the classification accuracy in Cotraining, particularly when one single view of training data is available. We also introduce an optimized algorithm to cope with time complexity of computing the global KNN, which enables tackling real classification problems.