Self-adaptive probability estimation for Naive Bayes classification

Jia Wu, Zhihua Cai, Xingquan Zhu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

38 Citations (Scopus)


Probability estimation from a given set of training examples is crucial for learning Naive Bayes (NB) Classifiers. For an insufficient number of training examples, the estimation will suffer from the zero-frequency problem which does not allow NB classifiers to classify instances whose conditional probabilities are zero. Laplace-estimate and M-estimate are two common methods which alleviate the zero-frequency problem by adding some fixed terms to the probability estimation to avoid zero conditional probability. A major issue with this type of design is that the fixed terms are pre-specified without considering the uniqueness of the underlying training data. In this paper, we propose an Artificial Immune System (AIS) based self-adaptive probability estimation method, namely AISENB, which uses AIS to automatically and self-adaptively select the optimal terms and values for probability estimation. The unique immune system based evolutionary computation process, including initialization, clone, mutation, and crossover, ensure that AISENB can adjust itself to the data without explicit specification of functional or distributional forms for the underlying model. Experimental results and comparisons on 36 benchmark datasets demonstrate that AISENB significantly outperforms traditional probability estimation based Naive Bayes classification approaches.
Original languageEnglish
Title of host publicationThe 2013 International Joint Conference on Neural Networks (IJCNN)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9781467361293
Publication statusPublished - 1 Aug 2013
Externally publishedYes
Event2013 International Joint Conference on Neural Networks, IJCNN 2013 - Dallas, United States
Duration: 4 Aug 20139 Aug 2013


Conference2013 International Joint Conference on Neural Networks, IJCNN 2013
Country/TerritoryUnited States


  • artificial immune systems
  • Bayes methods
  • belief networks
  • evolutionary computation
  • learning (artificial intelligence)
  • pattern classification
  • probability
  • Naive Bayes classification
  • Naive Bayes classifier learning
  • zero-frequency problem
  • NB classifiers
  • Laplace-estimate
  • M-estimate
  • zero conditional probability
  • artificial immune system based self-adaptive probability estimation method
  • AIS based self-adaptive probability estimation method
  • immune system based evolutionary computation process
  • distributional form specification
  • functional form specification
  • Vectors
  • Estimation
  • Niobium
  • Immune system
  • Sociology
  • Statistics
  • Cloning


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