关键词: 三支决策, 朴素贝叶斯, 增量算法, 分类确信度, 边界域

Abstract:

Incremental learning is a kind of method that updating the classification model by modifying the parameters with the useful information in the incremental data. The Naïve Bayes algorithm is one of the best selections of incremental learning for its characteristics of natural utilizing the prior information and incremental information. The three-way decision is a promising theory proposed in recent years, which conforms to the human cognitive model with the own subjective characteristics. A novel Naïve Bayesian incremental learning algorithm based on the three-way decision is proposed in this paper, which merges the thought of three-way decision into the Naïve Bayesian model. A classification particular factor is firstly defined and applied to determine the positive, deferment, and negative regions by combining with the cost function. Then the three-region information is constructed the newly Naïve Bayesian incremental learning algorithm. The experimental results show that the classification accuracy and recall rate of this method is significantly improved when the thresholds α and β are determined appropriately.

Key words: three-way decision, Naïve Bayes, incremental algorithm, categorical sureness, deferment region