Feature selection via fuzzy clustering

被引：0

作者：

Sun, Hao-Jun ^{[1
]}

Sun, Mei ^{[1
]}

Mei, Zhen ^{[2
]}

机构：

[1] Hebei Univ, Coll Math & Comp Sci, Baoding 071002, Peoples R China

[2] Manifold Data Min, Toronto, ON M6N 2J1, Canada

来源：

PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年

关键词：

feature selection; clustering; classification error rate; Fuzzy C-Means;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper deals with feature selection for classification with wrapper framework. We develop a new algorithm for feature selection, based on a fuzzy clustering technique and an iterative process verifying classification accuracy. By monitoring discrepancy between two cluster systems, one derived with full features of the dataset, the other one with a subset of features, we are able to evaluate representation power of the subset of features with respect to the original feature set. Experimental results confirm efficiency of the proposed algorithm.

引用

页码：1400 / +

页数：2

共 50 条

[31] A Framework for Feature Selection in Clustering
Witten, Daniela M.
Tibshirani, Robert
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2010, 105 (490) : 713 - 726
[32] Localized feature selection for clustering
Li, Yuanhong
Dong, Ming
Hua, Jing
PATTERN RECOGNITION LETTERS, 2008, 29 (01) : 10 - 18
[33] Feature selection for hierarchical clustering
Questier, F
Walczak, B
Massart, DL
Boucon, C
de Jong, S
ANALYTICA CHIMICA ACTA, 2002, 466 (02) : 311 - 324
[34] Image Segmentation via Feature Weighted Fuzzy Clustering by a DCA Based Algorithm
Hoai Minh Le
Bich Thuy Nguyen Thi
Minh Thuy Ta
Hoai An Le Thi
ADVANCED COMPUTATIONAL METHODS FOR KNOWLEDGE ENGINEERING, 2013, 479 : 53 - 63
[35] Interactive and Complementary Feature Selection via Fuzzy Multigranularity Uncertainty Measures
Wan, Jihong
Chen, Hongmei
Li, Tianrui
Yuan, Zhong
Liu, Jia
Huang, Wei
IEEE TRANSACTIONS ON CYBERNETICS, 2023, 53 (02) : 1208 - 1221
[36] Unsupervised Feature Selection Based on Fuzzy Clustering for Fault Detection of the Tennessee Eastman Process
Bedoya, C.
Uribe, C.
Isaza, C.
ADVANCES IN ARTIFICIAL INTELLIGENCE - IBERAMIA 2012, 2012, 7637 : 350 - 360
[37] A Novel Intuitionistic Fuzzy Clustering Algorithm Based on Feature Selection for Multiple Object Tracking
Li, Liang-qun
Wang, Xiao-li
Liu, Zong-xiang
Xie, Wei-xin
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS, 2019, 21 (05) : 1613 - 1628
[38] Adaptive Fuzzy Exponent Cluster Ensemble System Based Feature Selection and Spectral Clustering
Ben Ayed, Abdelkarim
Ben Halima, Mohamed
Alimi, Adel M.
2017 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE), 2017,
[39] An improved method of fuzzy c-means clustering by using feature selection and weighting
Pourjabari, Amirhadi Jahanbakhsh
Seyedzadegan, Mojtaba
INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND NETWORK SECURITY, 2016, 16 (10): : 64 - 69
[40] A Novel Intuitionistic Fuzzy Clustering Algorithm Based on Feature Selection for Multiple Object Tracking
Liang-qun Li
Xiao-li Wang
Zong-xiang Liu
Wei-xin Xie
International Journal of Fuzzy Systems, 2019, 21 : 1613 - 1628

← 1 2 3 4 5 →