Bayesian probability

Statistics

Support vector machine

Bayes' theorem

Weighting

Independence (probability theory)

Data mining

Computer science

Conditional independence

Radiology

Naive Bayes classifier

Pattern recognition (psychology)

Mathematics

Artificial intelligence

Medicine

Machine learning

Pattern Recognition

Naive Bayes (NB) continues to be one of the top 10 data mining algorithms, but its conditional independence assumption rarely holds true in real-world applications. Therefore, many different categories of improved approaches, including attribute weighting and instance weighting, have been proposed to alleviate this assumption. However, few of these approaches simultaneously pay attention to attribute weighting and instance weighting. In this study, we propose a new improved model called attribute and instance weighted naive Bayes (AIWNB), which combines attribute weighting with instance weighting into one uniform framework. In AIWNB, the attribute weights are incorporated into the naive Bayesian classification formula, and then the prior and conditional probabilities are estimated using instance weighted training data. To learn instance weights, we single out an eager approach and a lazy approach, and thus two different versions are created, which we denote as AIWNBE and AIWNBL, respectively. Extensive experimental results show that both AIWNBE and AIWNBL significantly outperform NB and all the other existing state-of-the-art competitors.

Attribute and instance weighted naive Bayes