Accurate probability-based ranking of instances is crucial in many real-world data mining applications. KNN (k-nearest neighbor) [1] has been intensively studied as an effective c...
Probability theory is the framework for making decision under uncertainty. In classification, Bayes' rule is used to calculate the probabilities of the classes and it is a bi...
Mohammed J. Islam, Q. M. Jonathan Wu, Majid Ahmadi...
In this paper, we report our work on spam filtering with three novel bayesian classification methods: Aggregating One-Dependence Estimators (AODE), Hidden Naïve Bayes (HNB), Loca...
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 o...
Naive Bayes is an effective and efficient learning algorithm in classification. In many applications, however, an accurate ranking of instances based on the class probability is m...