An empirical analysis of attribute skewness over class imbalance on Probabilistic Neural Network and Na?ve Bayes classifier

Many real world data are subject to skewness or imbalance. Often class distribution is imbalanced, while several attribute or feature skewness is also frequent. Skewness affects the classification of the dataset samples. While class skewness biases the classification towards majority classes, skewed features may also bias the classification as they are significant for few classes. The purpose of this paper is to find out the impact of skewed feature variation in the training dataset for the Naïve Bayesian Classifier(NBC) and Probabilistic Neural Network(PNN) while classifying imbalanced data. The experiment was carried out on six KEEL dataset which are skewed in terms of class distribution having different imbalance ratio. This work looked for skewed features in those dataset and analysed the classification performance with and without the skewed features. The result illustrates that NBC is better in the mentioned circumstance compared to PNN.