Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks
作者：
Highlights：
• The hybrid decision tree is able to remove noisy data to avoid overfitting.
• The hybrid Bayes classifier identifies a subset of attributes for classification.
• Both algorithms are evaluated using 10 real benchmark datasets.
• They outperform traditional classifiers in challenging multi-class applications.
摘要
•The hybrid decision tree is able to remove noisy data to avoid overfitting.•The hybrid Bayes classifier identifies a subset of attributes for classification.•Both algorithms are evaluated using 10 real benchmark datasets.•They outperform traditional classifiers in challenging multi-class applications.
论文关键词：Data mining,Classification,Hybrid,Decision tree,Naïve Bayes classifier
论文评审过程：Available online 11 September 2013.
论文官网地址：https://doi.org/10.1016/j.eswa.2013.08.089
