Semi-Supervised Fuzzy-Rough Feature Selection

Richard Jensen*, Sarah Vluymans, Neil MacParthalain, Chris Cornelis, Yvan Saeys

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

9 Citations (SciVal)

Abstract

With the continued and relentless growth in dataset sizes in recent times, feature or attribute selection has become a necessary step in tackling the resultant intractability. Indeed, as the number of dimensions increases, the number of corresponding data instances required in order to generate accurate models increases exponentially. Fuzzy-rough set-based feature selection techniques offer great flexibility when dealing with real-valued and noisy data; however, most of the current approaches focus on the supervised domain where the data object labels are known. Very little work has been carried out using fuzzy-rough sets in the areas of unsupervised or semi-supervised learning. This paper proposes a novel approach for semi-supervised fuzzy-rough feature selection where the object labels in the data may only be partially present. The approach also has the appealing property that any generated subsets are also valid (super)reducts when the whole dataset is labelled. The experimental evaluation demonstrates that the proposed approach can generate stable and valid subsets even when up to 90% of the data object labels are missing.

Original languageEnglish
Title of host publicationRough Sets, Fuzzy Sets, Data Mining, and Granular Computing
Subtitle of host publication15th International Conference, RSFDGrC
EditorsYiyu Yao, Qinghua Hu, Hong Yu, Jerzy W. Grzymala-Busse
PublisherSpringer Nature
Pages185-195
Number of pages11
ISBN (Print)978-3-319-25782-2, 331925782X
DOIs
Publication statusPublished - 15 Dec 2015
Event15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC) - Tianjin
Duration: 20 Nov 201523 Nov 2015

Publication series

NameLecture Notes in Artificial Intelligence
PublisherSPRINGER-VERLAG BERLIN
Volume9437
ISSN (Print)0302-9743

Conference

Conference15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC)
CityTianjin
Period20 Nov 201523 Nov 2015

Keywords

  • Fuzzy-rough sets
  • Feature selection
  • Semi-supervised learning

Fingerprint

Dive into the research topics of 'Semi-Supervised Fuzzy-Rough Feature Selection'. Together they form a unique fingerprint.

Cite this