Improving Naive Bayes Classifier Using Conditional Probabilities

Learning the naive Bayes classifier with optimization models

Naive Bayes is among the simplest probabilistic classifiers. It often performs surprisingly well in many real world applications, despite the strong assumption that all features are conditionally independent given the class. In the learning process of this classifier with the known structure, class probabilities and conditional probabilities are calculated using training data, and then values o...

Boosting the Tree Augmented Naïve Bayes Classifier

The Tree Augmented Naïve Bayes (TAN) classifier relaxes the sweeping independence assumptions of the Naïve Bayes approach by taking account of conditional probabilities. It does this in a limited sense, by incorporating the conditional probability of each attribute given the class and (at most) one other attribute. The method of boosting has previously proven very effective in improving the per...

Perplexed Bayes Classifier

Naive Bayes classifiers estimate posterior probabilities poorly (Zhang, 2004). In this paper, we propose a modification to the Naive Bayes classification algorithm which improves the classifier’s posterior probability estimates without affecting its performance. Since the modification involves the use of the reciprocal of the perplexity of the class-conditional feature probabilities, we call th...

Naive Bayes and Text Classification I - Introduction and Theory

2 Naive Bayes Classification 3 2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 2.2 Posterior Probabilities . . . . . . . . . . . . . . . . . . . . . . . . 3 2.3 Class-conditional Probabilities . . . . . . . . . . . . . . . . . . . 5 2.4 Prior Probabilities . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.5 Evidence . . . . . . . . . . . . . . . . . . . . . . ...

Improving Naive Bayes Using Class-Conditional ICA