An Improved Naive Bayes Classifier on Imbalanced Attributes

摘要

Data plays a major and prominent role in this modern information era. Classification is a data mining task to discover the hidden information from large amounts of data stored in the repository. This process becomes extremely challenging in case of highly imbalanced dataset. Prediction from imbalanced attributes cannot be done accurately in the following case: During the training phase, the categorical variable is not observed but the test phase encounters the categorical variable and hence it assigns zero probability which leads to false prediction. To overcome this scenario, this article proposes a novel smoothing technique called optimized laplace smoothing estimation. This technique adds a bias value function to improve the accuracy of imbalanced attributes. For example, a child dataset has more attributes and the classification model is used to predict the child weight. Some of the attribute values may not be present in the child dataset due to which Naive Bayes assigns a zero for incomplete and an empty attribute. This leads to inaccurate prediction. In such cases, Naive Bayes can be further tuned by adding some new parameters as well as altering the existing optimization method. Experimental analysis shows that this novel smoothing technique enhances the classification accuracy by means of accurate predictions for imbalanced attributes.