Naive Bayes Classification Algorithm Based on Optimized Training Data

Naive Bayes classification algorithm is an effective simple classification algorithm. Most researches in traditional Naive Bayes classification focus on the improvement of the classification algorithm, ignoring the selection of training data which has a great effect on the performance of classifier. And so a method is proposed to optimize the selection of training data in this paper. Adopting this method, the noisy instances in training data are eliminated by user-defined effectiveness threshold, improving the performance of classifier. Experimental results on large-scale data show that our approach significantly outperforms the baseline classifier.