Fuzzy Decision Based Algorithm for Classifying Incomplete Data

Fudong Nian; Jun Wu; Teng Li; Feifeng Li

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Fuzzy Decision Based Algorithm for Classifying Incomplete Data

Fudong Nian 1 #,Jun Wu 2,Teng Li 1 *,Feifeng Li 3

1.Department of Electrical Engineering and Automation, Anhui Univeristy, HeFei 230601

2.Institute of Software Application Technology & Chinese Academy of Sciences, Guangzhou 511458

3.Department of Economics, Huainan Union Unioversity, Huainan 232001

*Correspondence author

#Submitted by

Subject:

Funding: the Ph.D. Programs Foundation of Ministry of Education of China （No.No. 20133401120005）, the Open Project Program of the National Laboratory of Pattern Recognition （No.NLPR)）, NSF of China （No.No. 61300056）, Anhui Provincial Natural Science Foundation of China （No.No. 1408085QF118）

Opened online:22 September 2014

Accepted by: none

Citation: Fudong Nian,Jun Wu,Teng Li.Fuzzy Decision Based Algorithm for Classifying Incomplete Data[OL]. [22 September 2014] http://en.paper.edu.cn/en_releasepaper/content/4609074

Classification is a very important research topic. But in real world application the incomplete data usually exist. The incompleteness of data degrades the models learning quality in classification. The classification problem can be separated into two phases: learning phase and classification phase. Most previous methods dealing with incomplete data only focus on handling incomplete data in the learning phase. For the incomplete value appearing in the classification phase, most of the current approaches cannot work or perform badly. In this paper a novel classifier is proposed to solve the incomplete data classification problem. In contrast to the conventional boosting algorithm which uses a deterministic decision method during the iterations, without considering the noise in the data set sufficiently, we propose a new boosting algorithm using fuzzy decisions for every hypothesis at the iterations of the boosting scheme. It selects the data events from a dataset, and then combines them. The experimental results demonstrate the superories of the proposed strategies for solving incomplete data problem.

Keywords:Pattern classification, incomplete data, decision tree, boosting

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