Instance Cloning Local Naive Bayes

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...

• Type: Conference
• Year: 2005
• Where: AI
• Authors: Liangxiao Jiang, Harry Zhang, Jiang Su
• Keywords: AI 2005, Artificial Intelligence, Naive Bayes, Strong Dependences, Test Instance