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Attribute weighting for averaged one-dependence estimators

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Abstract

Averaged one-dependence estimators (AODE) is a type of supervised learning algorithm that relaxes the conditional independence assumption that governs standard naïve Bayes learning algorithms. AODE has demonstrated reasonable improvement in terms of classification performance when compared with a naïve Bayes learner. However, AODE does not consider the relationships between the super-parent attribute and other normal attributes. In this paper, we propose a novel method based on AODE that weighs the relationship between the attributes called weighted AODE (WAODE), which is an attribute weighting method that uses the conditional mutual information metric to rank the relations among the attributes. We have conducted experiments on University of California, Irvine (UCI) benchmark datasets and compared accuracies between AODE and our proposed learner. The experimental results in our paper show that WAODE exhibits higher accuracy performance than the original AODE.

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Acknowledgment

This work was supported by the Korea Creative Content Agency (KOCCA) grant funded by the Korea government (no. R2015090021).

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Authors and Affiliations

  1. Department of Computer Software Institute, Weifang University of Science and Technology, Shouguang, 262-700, Shandong, China

    Zhong-Liang Xiang

  2. Department of Computer Engineering, Dongseo University, 47, Churye-Ro, Sasang-Gu, Busan, 47011, Republic of Korea

    Dae-Ki Kang

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  1. Zhong-Liang Xiang
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  2. Dae-Ki Kang
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Correspondence to Dae-Ki Kang.

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Xiang, ZL., Kang, DK. Attribute weighting for averaged one-dependence estimators. Appl Intell 46, 616–629 (2017). https://doi.org/10.1007/s10489-016-0854-3

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  • DOI: https://doi.org/10.1007/s10489-016-0854-3

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