Computer Engineering and Applications ›› 2020, Vol. 56 ›› Issue (18): 42-49.DOI: 10.3778/j.issn.1002-8331.1908-0015

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Novel Naïve Bayesian Incremental Learning Algorithm Based on Three-Way Decisions

HAN Suqing, CHENG Huiwen, WANG Baoli   

  1. 1.Department of Computer Science and Technology, Taiyuan Normal University, Jinzhong, Shanxi 030619, China
    2.School of Mathematics and Information Technology, Yuncheng University, Yuncheng, Shanxi 044000, China
  • Online:2020-09-15 Published:2020-09-10



  1. 1.太原师范学院 计算机科学与技术系,山西 晋中 030619
    2.运城学院 数学与信息技术学院,山西 运城 044000


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



关键词: 三支决策, 朴素贝叶斯, 增量算法, 分类确信度, 边界域