Instance Cloning Local Naive Bayes

Abstract

The instance-based k-nearest neighbor algorithm (KNN)[1] is an effective classification model. Its classification is simply based on a vote within the neighborhood, consisting of k nearest neighbors of the test instance. Recently, researchers have been interested in deploying a more sophisticated local model, such as naive Bayes, within the neighborhood. It is expected that there are no strong dependences within the neighborhood of the test instance, thus alleviating the conditional independence assumption of naive Bayes. Generally, the smaller size of the neighborhood (the value of k), the less chance of encountering strong dependences. When k is small, however, the training data for the local naive Bayes is small and its classification would be inaccurate. In the currently existing models, such as LWNB [3], a relatively large k is chosen. The consequence is that strong dependences seem unavoidable.

In our opinion, a small k should be preferred in order to avoid strong dependences. We propose to deal with the problem of lack of local training data using sampling (cloning). Given a test instance, clones of each instance in the neighborhood is generated in terms of its similarity to the test instance and added to the local training data. Then, the local naive Bayes is trained from the expanded training data. Since a relatively small k is chosen, the chance of encountering strong dependences within the neighborhood is small. Thus the classification of the resulting local naive Bayes would be more accurate. We experimentally compare our new algorithm with KNN and its improved variants in terms of classification accuracy, using the 36 UCI datasets recommended by Weka [8], and the experimental results show that our algorithm outperforms all those algorithms significantly and consistently at various k values.