Clustering-based Feature Selection in Semi-supervised Problems

被引：10

作者：

Quinzan, Ianisse ^{[1
]}

Sotoca, Jose M. ^{[1
]}

Pla, Filiberto ^{[1
]}

机构：

[1] Univ Jaume 1, Inst Noves Tecnol Imatge, Dept Llenguatges & Sistemes Informat, Castellon De La Plana, Spain

来源：

2009 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS DESIGN AND APPLICATIONS | 2009年

关键词：

Semi-supervised learning; feature selection; information measures; MUTUAL INFORMATION;

D O I：

10.1109/ISDA.2009.211

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this contribution a feature selection method in semi-supervised problems is proposed. This method selects variables using a feature clustering strategy, using a combination of supervised and unsupervised feature distance measure, which is based on Conditional Mutual Information and Conditional Entropy. Real databases were analyzed with different ratios between labelled and unlabelled samples in the training set, showing the satisfactory behaviour of the proposed approach.

引用

页码：535 / 540

页数：6

共 50 条

[1] Feature selection and semi-supervised clustering using multiobjective optimization
Saha, Sriparna
Ekbal, Asif
Alok, Abhay Kumar
Spandana, Rachamadugu
SPRINGERPLUS, 2014, 3
[2] Feature Selection and Semi-supervised Clustering Using Multiobjective Optimization
Alok, Abhay Kumar
Saha, Sriparna
Ekbal, Asif
2014 INTERNATIONAL CONFERENCE ON SOFT COMPUTING & MACHINE INTELLIGENCE ISCMI 2014, 2014, : 126 - 129
[3] A graph Laplacian based approach to semi-supervised feature selection for regression problems
Doquire, Gauthier
Verleysen, Michel
NEUROCOMPUTING, 2013, 121 : 5 - 13
[4] Graph Laplacian for Semi-supervised Feature Selection in Regression Problems
Doquire, Gauthier
Verleysen, Michel
Advances in Computational Intelligence, IWANN 2011, Pt I, 2011, 6691 : 248 - 255
[5] Weighting Based Approach for Semi-supervised Feature Selection
Benabdeslem, Khalid
Hindawi, Mohammed
Makkhongkaew, Raywat
NEURAL INFORMATION PROCESSING, ICONIP 2015, PT IV, 2015, 9492 : 300 - 307
[6] Clustering-Based Transductive Semi-Supervised Learning for Learning-to-Rank
Rahangdale, Ashwini
Raut, Shital
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2019, 33 (12)
[7] Forward semi-supervised feature selection
Ren, Jiangtao
Qiu, Zhengyuan
Fan, Wei
Cheng, Hong
Yu, Philip S.
ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2008, 5012 : 970 - +
[8] Semi-supervised sentiment classification based on sentiment feature clustering
Li, Suke
Jiang, Yanbing
Jisuanji Yanjiu yu Fazhan/Computer Research and Development, 2013, 50 (12): : 2570 - 2577
[9] Semi-Supervised Clustering Algorithm Based on Deep Feature Mapping
Xu, Xiong
Zhou, Chun
Wang, Chenggang
Zhang, Xiaoyan
Meng, Hua
INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2023, 37 (01): : 815 - 831
[10] A Feature Space Learning Model Based on Semi-Supervised Clustering
Guan, Renchu
Wang, Xu
Marchese, Maurizio
Liang, Yanchun
Yang, Chen
2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE) AND IEEE/IFIP INTERNATIONAL CONFERENCE ON EMBEDDED AND UBIQUITOUS COMPUTING (EUC), VOL 1, 2017, : 403 - 409

← 1 2 3 4 5 →