A novel Naive Bayes model: Packaged Hidden Naive Bayes
Naive Bayes classifier has good performance on many datasets, however, the performance is very poor on some datasets which have a strong correlation between attributes due to the conditional independence assumption is not always true in the real world. In the latest Hidden Naive Bayes (HNB) algorithm, each attribute corresponds to a hidden parent which combines the influences of all other attributes. Compared to other Bayesian algorithms, its performance is significantly improved, but too much test time on high-dimensional datasets cost. In this paper, to find the optimal combination between Naive Bayes and HNB, a novel model Packaged Hidden Naive Bayes (PHNB), which the number of attributes in the hidden parent is controlled through packaging idea, is proposed. Our experiments show that compared to HNB, PHNB significantly reduces the test time on many high-dimensional datasets, and has higher accuracy on some particular datasets.
Naive Bayes HNB classification test time
Yaguang Ji Songnian Yu Yafeng Zhang
School of Computer Engineering & Science, Shanghai University Yanchang Rd, 149, 200072 Shanghai,Chin School of Computer Engineering & Science, Shanghai University YanchangRd, 149, 200072 Shanghai,China
国际会议
重庆
英文
987-990
2011-08-20（万方平台首次上网日期，不代表论文的发表时间）