A New Three-Way Incremental Naive Bayes Classifier

Aiming at the problems of the dynamic increase in data in real life and that the naive Bayes (NB) classifier only accepts or rejects the sample processing results, resulting in a high error rate when dealing with uncertain data, this paper combines three-way decision and incremental learning, and a new three-way incremental naive Bayes classifier (3WD-INB) is proposed. First, the NB classifier is established, and the distribution fitting is carried out according to the minimum residual sum of squares (RSS) for continuous data, so that 3WD-INB can process both discrete data and continuous data, then carry out an incremental learning operation, select the samples with higher data quality according to the confidence of the samples in the incremental training set for incremental learning, solve the problem of data dynamics and filter the poor samples. Then we construct the 3WD-INB classifier and determine the classification rules of the positive, negative and boundary domains of the 3WD-INB classifier, so that the three-way classification of samples can be realized and better decisions can be made when dealing with uncertain data. Finally, five discrete data and five continuous data are selected for comparative experimental analysis with traditional classification methods. The results show that 3WD-INB has high accuracy and recall rate on different types of datasets, and the classification performance is also relatively stable.

Automated summary

This paper proposes a new three-way incremental naive Bayes classifier (3WD-INB) by combining three-way decision and incremental learning to tackle the problems of increasing data in real life and high error rates in handling uncertain data by the naive Bayes (NB) classifier. The 3WD-INB is able to process both discrete and continuous data by establishing the NB classifier and carrying out distribution fitting for continuous data. Incremental learning operation is then performed by selecting samples with higher data quality in the incremental training set, solving the problem of data dynamics and filtering poor samples. Experimental analysis shows that 3WD-INB achieves high accuracy and recall rate on different types of datasets, demonstrating its effectiveness in classification performance.