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13 December 2021 Research on naive Bayes algorithm based on feature weighted
Dan Zhang, Yinglan Fang, Hui Tang
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Proceedings Volume 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021); 120871A (2021) https://doi.org/10.1117/12.2624729
Event: International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 2021, Kunming, China
Abstract
The traditional naive bayes algorithm is a commonly used text classification algorithm, it’s attribute independence assumptions reduce the classification effect of text to some extent. In response to this problem, a weighted naive bayes text classification algorithm based on improved feature weight was proposed. First, when the TFIDF algorithm was improved from the inverse characteristic frequency, the category frequency, etc., the redundant attribute was removed, and the weight of the different feature items are used to measure the weight of different feature items, and then use the cross entropy. The feature item weight was substituted into a naive bayes formula, and the weighted naive bayes classification algorithm was constructed. Compared with several different algorithms, the experimental results show that this algorithm has significant increase in precision, recall and F1 score.
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Dan Zhang, Yinglan Fang, and Hui Tang "Research on naive Bayes algorithm based on feature weighted", Proc. SPIE 12087, International Conference on Electronic Information Engineering and Computer Technology (EIECT 2021), 120871A (13 December 2021); https://doi.org/10.1117/12.2624729
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KEYWORDS
Feature extraction
Distance measurement
Analytical research
Data modeling
Information technology
Machine learning
Mining
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