Analysis of decision tree classification algorithm based on attribute reduction and application in criminal behavior

Author

Hui, Wang ; Jing, Wang ; Tao, Zheng

Author_Institution

Nat. Eng. Res. Center of Adv. Rolling, Univ. of Sci. & Technol. Beijing, Beijing, China

Volume

1

fYear

2011

fDate

11-13 March 2011

Firstpage

27

Lastpage

30

Abstract

In this paper, the attribute reduction strategy is syncretized into classification algorithm of the decision tree and applied to criminal behavior analysis. Finding implicit knowledge in the criminal database by this method can utilized to assist making decision for police quickly and accurately. The classification algorithm of the decision tree based on rough set is proposed for multi-attribute data table. The scale of decision tree and branches is minished and the reliability is improved via attribute reduction. Successful application in the analysis of criminal behavior shows the feasibility of the algorithm.

Keywords

behavioural sciences computing; data reduction; decision making; decision trees; pattern classification; rough set theory; attribute reduction; classification algorithm; criminal behavior analysis; criminal database; decision making; decision tree; multi-attribute data table; rough set; Algorithm design and analysis; Classification algorithms; Data mining; Decision trees; Educational institutions; Presses; Training; attribute reduction; data mining; decision tree;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Research and Development (ICCRD), 2011 3rd International Conference on

Conference_Location

Shanghai

Print_ISBN

978-1-61284-839-6

Type

conf

DOI

10.1109/ICCRD.2011.5763966

Filename

5763966