A Novel Hybrid Approach: Instance Weighted Hidden Naive Bayes

Liangjun Yu, Shengfeng Gan, Yu Chen and Dechun Luo
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Liangjun Yu: College of Computer, Hubei University of Education, Wuhan 430205, China
Shengfeng Gan: College of Computer, Hubei University of Education, Wuhan 430205, China
Yu Chen: College of Computer, Hubei University of Education, Wuhan 430205, China
Dechun Luo: School of Management, Huazhong University of Science and Technology, Wuhan 430071, China

Mathematics, 2021, vol. 9, issue 22, 1-15

Abstract: Naive Bayes (NB) is easy to construct but surprisingly effective, and it is one of the top ten classification algorithms in data mining. The conditional independence assumption of NB ignores the dependency between attributes, so its probability estimates are often suboptimal. Hidden naive Bayes (HNB) adds a hidden parent to each attribute, which can reflect dependencies from all the other attributes. Compared with other Bayesian network algorithms, it offers significant improvements in classification performance and avoids structure learning. However, the assumption that HNB regards each instance equivalent in terms of probability estimation is not always true in real-world applications. In order to reflect different influences of different instances in HNB, the HNB model is modified into the improved HNB model. The novel hybrid approach called instance weighted hidden naive Bayes (IWHNB) is proposed in this paper. IWHNB combines instance weighting with the improved HNB model into one uniform framework. Instance weights are incorporated into the improved HNB model to calculate probability estimates in IWHNB. Extensive experimental results show that IWHNB obtains significant improvements in classification performance compared with NB, HNB and other state-of-the-art competitors. Meanwhile, IWHNB maintains the low time complexity that characterizes HNB.

Keywords: Bayesian network; hidden naive Bayes; instance weighting (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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