10 papers analyzed
These studies suggest that various enhancements to the naive Bayes algorithm, such as selective attribute adoption, hidden variables, text normalization, feature weighting, and integration with other methods, can significantly improve its classification accuracy and performance in specific tasks.
Naive Bayes is a widely used algorithm in data mining and machine learning due to its simplicity, efficiency, and effectiveness. Despite its assumption of attribute independence, which is often violated in real-world data, various improvements and adaptations have been proposed to enhance its performance across different tasks.
Selective Naive Bayes:
Hidden Naive Bayes (HNB):
Naive Bayes for Regression:
Text Classification Enhancements:
Relational Learning:
Non-Parametric Naive Bayes:
Naive Bayes remains a popular and effective algorithm in various domains due to its simplicity and efficiency. However, its performance can be significantly enhanced through selective attribute use, hidden parent structures, feature weighting, and integration with other models. These improvements address the limitations of the independence assumption and adapt Naive Bayes for more complex tasks such as regression and text classification.
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