|Table of Contents|

Generalized cost-sensitive approximate attributereduction based on three-way decisions(PDF)

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2019年04期
Page:
481-488
Research Field:
Publishing date:

Info

Title:
Generalized cost-sensitive approximate attributereduction based on three-way decisions
Author(s):
Fang Yu1Gao Lei1Liu Zhonghui1Yang Xin2
1.School of Computer Science,Southwest Petroleum University,Chengdu 610500,China; 2. School of Economic Information Engineering,Southwestern University of Finance andEconomics,Chengdu 611130,China
Keywords:
attribute reduction cost-sensitive learning(in)discernibility granular computing three-way decisions
PACS:
TP181
DOI:
10.14177/j.cnki.32-1397n.2019.43.04.015
Abstract:
In the research spectrum of rough sets,the primary task of attribute reduction is to obtain a minimum subset of attribute set while maintaining the relevant features of the original data. The goal of cost-sensitive learning is to minimize the various costs. The significance of approximate attribute reduction is to enable decision makers to leverage the cost tolerance and the grade of knowledge discovery. This paper proposes a cost-sensitive approximate attribute reduction problem with both qualitative and quantitative criteria. The qualitative criteria refers to(in)discernibility,and the quantitative criteria refers to approximate parameters ε and costs. Based on the three-way decisions and discernible matrix,this paper portrays a framework to solve the problem of cost-sensitive approximate attribute reduction. First,we define the quality function of attribute set which explains the multi-granularity structure. Second,we propose a‘cost-performance index’fitness function to evaluate the importance of attribute,then the proposed fitness function and (α,β) thresholds pair with three-way decisions are applied to tri-partition the attribute sets. Finally,we design the two algorithms(deletion-based and addition-based)to tackle the reduction problem. The validity of the algorithms is verified by the experimental result analysis,which proves that our framework has broader adaptability and applicability.

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Last Update: 2019-09-30