NDLI: A naive Bayes probability estimation model based on self-adaptive differential evolution

Content Provider	Springer Nature Link
Author	Wu, Jia Cai, Zhihua
Copyright Year	2013
Abstract	In the process of learning the naive Bayes, estimating probabilities from a given set of training samples is crucial. However, when the training samples are not adequate, probability estimation method will inevitably suffer from the zero-frequency problem. To avoid this problem, Laplace-estimate and M-estimate are the two main methods used to estimate probabilities. The estimation of two important parameters m (integer variable) and p (probability variable) in these methods has a direct impact on the underlying experimental results. In this paper, we study the existing probability estimation methods and carry out a parameter Cross-test by experimentally analyzing the performance of M-estimate with different settings for the two parameters m and p. This part of experimental result shows that the optimal parameter values vary corresponding to different data sets. Motivated by these analysis results, we propose an estimation model based on self-adaptive differential evolution. Then we propose an approach to calculate the optimal m and p value for each conditional probability to avoid the zero-frequency problem. We experimentally test our approach in terms of classification accuracy using the 36 benchmark machine learning repository data sets, and compare it to a naive Bayes with Laplace-estimate and M-estimate with a variety of setting of parameters from literature and those possible optimal settings via our experimental analysis. The experimental results show that the estimation model is efficient and our proposed approach significantly outperforms the traditional probability estimation approaches especially for large data sets (large number of instances and attributes).
Starting Page	671
Ending Page	694
Page Count	24
File Format	PDF
ISSN	09259902
Journal	Journal of Intelligent Information Systems
Volume Number	42
Issue Number	3
e-ISSN	15737675
Language	English
Publisher	Springer US
Publisher Date	2013-09-29
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Naive Bayes Probability estimation M-estimate Laplace-estimate Differential evolution Self-adaptive Classification Information Storage and Retrieval Data Structures, Cryptology and Information Theory Artificial Intelligence (incl. Robotics) Business Information Systems Document Preparation and Text Processing
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Networks and Communications Information Systems Software Hardware and Architecture

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