A combined classification algorithm based on C4.5 and NB

Liangxiao Jiang, Chaoqun Li, Jia Wu, Jian Zhu

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

6 Citations (Scopus)

Abstract

When our learning task is to build a model with accurate classification, C4.5 and NB are two very important algorithms for achieving this task because of their simplicity and high performance. In this paper, we present a combined classification algorithm based on C4.5 and NB, simply C4.5-NB. In C4.5-NB, the class probability estimates of C4.5 and NB are weighted according to their classification accuracy on the training data. We experimentally tested C4.5-NB in Weka system using the whole 36 UCI data sets selected by Weka, and compared it with C4.5 and NB. The experimental results show that C4.5-NB significantly outperforms C4.5 and NB in terms of classification accuracy. Besides, we also observe the ranking performance of C4.5-NB in terms of AUC (the area under the Receiver Operating Characteristics curve). Fortunately, C4.5-NB also significantly outperforms C4.5 and NB.
Original languageEnglish
Title of host publicationAdvances in Computation and Intelligence
Subtitle of host publicationThird International Symposium, ISICA 2008, Proceedings
EditorsLishan Kang, Zhihua Cai, Xuesong Yan, Yong Liu
Place of PublicationBerlin
PublisherSpringer, Springer Nature
Pages350-359
Number of pages10
ISBN (Electronic)9783540921370
ISBN (Print)9783540921363
DOIs
Publication statusPublished - 2008
Externally publishedYes
Event3rd International Symposium on Intelligence Computation and Applications (ISICA 2008) - Wuhan, China
Duration: 19 Dec 200821 Dec 2008

Conference

Conference3rd International Symposium on Intelligence Computation and Applications (ISICA 2008)
CountryChina
CityWuhan
Period19/12/0821/12/08

Keywords

  • decision trees
  • naive Bayes
  • combined algorithms
  • weights
  • classification
  • ranking
  • data mining

Cite this

Jiang, L., Li, C., Wu, J., & Zhu, J. (2008). A combined classification algorithm based on C4.5 and NB. In L. Kang, Z. Cai, X. Yan, & Y. Liu (Eds.), Advances in Computation and Intelligence: Third International Symposium, ISICA 2008, Proceedings (pp. 350-359). Berlin: Springer, Springer Nature. https://doi.org/10.1007/978-3-540-92137-0_39