Neighborhood rough set based heterogeneous feature subset selection

被引:1407
作者
Hu, Qinghua [1 ]
Yu, Daren [1 ]
Liu, Jinfu [1 ]
Wu, Congxin [1 ]
机构
[1] Harbin Inst Technol, Harbin 150001, Peoples R China
基金
中国国家自然科学基金;
关键词
categorical feature; numerical feature; heterogeneous feature; feature selection; neighborhood; rough sets;
D O I
10.1016/j.ins.2008.05.024
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 [计算机科学与技术];
摘要
Feature subset selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Most of researches are focused on dealing with homogeneous feature selection, namely, numerical or categorical features. In this paper, we introduce a neighborhood rough set model to deal with the problem of heterogeneous feature subset selection. As the classical rough set model can just be used to evaluate categorical features, we generalize this model with neighborhood relations and introduce a neighborhood rough set model. The proposed model will degrade to the classical one if we specify the size of neighborhood zero. The neighborhood model is used to reduce numerical and categorical features by assigning different thresholds for different kinds of attributes. In this model the sizes of the neighborhood lower and upper approximations of decisions reflect the discriminating capability of feature subsets. The size of lower approximation is computed as the dependency between decision and condition attributes. We use the neighborhood dependency to evaluate the significance of a subset of heterogeneous features and construct forward feature subset selection algorithms. The proposed algorithms are compared with some classical techniques. Experimental results show that the neighborhood model based method is more flexible to deal with heterogeneous data. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:3577 / 3594
页数:18
相关论文
共 47 条
[1]
[Anonymous], GRANULATION COMPUTIN
[2]
[Anonymous], P 4 INT S METH INT S
[3]
On fuzzy-rough sets approach to feature selection [J].
Bhatt, RB ;
Gopal, M .
PATTERN RECOGNITION LETTERS, 2005, 26 (07) :965-975
[4]
Blake C.L., 1998, UCI repository of machine learning databases
[5]
A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets [J].
Chen Degang ;
Wang Changzhong ;
Hu Qinghua .
INFORMATION SCIENCES, 2007, 177 (17) :3500-3518
[6]
CLASS-DEPENDENT DISCRETIZATION FOR INDUCTIVE LEARNING FROM CONTINUOUS AND MIXED-MODE DATA [J].
CHING, JY ;
WONG, AKC ;
CHAN, KCC .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1995, 17 (07) :641-651
[7]
Consistency-based search in feature selection [J].
Dash, M ;
Liu, HA .
ARTIFICIAL INTELLIGENCE, 2003, 151 (1-2) :155-176
[8]
FAYYAD U, 1996, P 13 INT C MACH LEAR, P157
[9]
Hall M. A., 1999, Proceedings of the Twelfth International Florida AI Research Society Conference, P235
[10]
Hall M.A., 2000, Working Paper], DOI DOI 10.5555/645529.657793