Boosting the Discriminant Power of Naive Bayes
- URL: http://arxiv.org/abs/2209.09532v1
- Date: Tue, 20 Sep 2022 08:02:54 GMT
- Title: Boosting the Discriminant Power of Naive Bayes
- Authors: Shihe Wang, Jianfeng Ren, Xiaoyu Lian, Ruibin Bai, Xudong Jiang
- Abstract summary: We propose a feature augmentation method employing a stack auto-encoder to reduce the noise in the data and boost the discriminant power of naive Bayes.
The experimental results show that the proposed method significantly and consistently outperforms the state-of-the-art naive Bayes classifiers.
- Score: 17.43377106246301
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Naive Bayes has been widely used in many applications because of its
simplicity and ability in handling both numerical data and categorical data.
However, lack of modeling of correlations between features limits its
performance. In addition, noise and outliers in the real-world dataset also
greatly degrade the classification performance. In this paper, we propose a
feature augmentation method employing a stack auto-encoder to reduce the noise
in the data and boost the discriminant power of naive Bayes. The proposed stack
auto-encoder consists of two auto-encoders for different purposes. The first
encoder shrinks the initial features to derive a compact feature representation
in order to remove the noise and redundant information. The second encoder
boosts the discriminant power of the features by expanding them into a
higher-dimensional space so that different classes of samples could be better
separated in the higher-dimensional space. By integrating the proposed feature
augmentation method with the regularized naive Bayes, the discrimination power
of the model is greatly enhanced. The proposed method is evaluated on a set of
machine-learning benchmark datasets. The experimental results show that the
proposed method significantly and consistently outperforms the state-of-the-art
naive Bayes classifiers.
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