@inproceedings{93a057d2f7ea445dae001a5de34fd370,
title = "Semi-Supervised Fuzzy-Rough Feature Selection",
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.",
keywords = "Fuzzy-rough sets, Feature selection, Semi-supervised learning",
author = "Richard Jensen and Sarah Vluymans and Neil MacParthalain and Chris Cornelis and Yvan Saeys",
year = "2015",
month = dec,
day = "15",
doi = "10.1007/978-3-319-25783-9_17",
language = "English",
isbn = "978-3-319-25782-2",
series = "Lecture Notes in Artificial Intelligence",
publisher = "Springer Nature",
pages = "185--195",
editor = "Yiyu Yao and Qinghua Hu and Hong Yu and Grzymala-Busse, {Jerzy W.}",
booktitle = "Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing",
address = "Switzerland",
note = "15th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing (RSFDGrC) ; Conference date: 20-11-2015 Through 23-11-2015",
}