Classification Algorithm based on NB for Class Overlapping Problem

Autor:	Shouxiang Zhao, Ming Li, Haitao Xiong, Tongqiang Jiang
Rok vydání:	2013
Předmět:	Numerical Analysis Statistical classification Current (mathematics) Computational Theory and Mathematics Applied Mathematics Model learning Structure (category theory) Sample (statistics) Algorithm Class (biology) Analysis Computer Science Applications Mathematics
Zdroj:	Applied Mathematics & Information Sciences. 7:409-415
ISSN:	2325-0399 1935-0090
DOI:	10.12785/amis/072l05
Popis:	Class overlapping is thought as one of the toughest problems in data mining because the complex structure of data. The current classification algorithms show little consideration of this problem. So when using this traditional classification algorithms to resolve this problem, classification performance is not good for sample s in overlapping region. To meet this critical challenge, in this paper, we pay a systematic study on the class overlapping problem and propose a new classification algorithm based on NB for class overlapping problem (CANB). CANB uses NB to find class overlapping region and use this region and non-overlapping region in NB classification model learning separately. Experimental results on ben ch mark and real-world data sets demonstrate that CANB can improve the classification performances for class overlapping problem stably and effectively.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::0a12c7f2ebfba80d91041b7b71470bf6 https://doi.org/10.12785/amis/072l05 Zobrazit plný text záznamu