TY - JOUR
T1 - Weight selection strategies for ordered weighted average based fuzzy rough sets
AU - Vluymans, Sarah
AU - MacParthaláin, Neil
AU - Cornelis, Chris
AU - Saeys, Yvan
N1 - Funding Information:
The research of Sarah Vluymans is funded by the Special Research Fund (BOF) of Ghent University. Yvan Saeys is an ISAC Marylou Ingram Scholar. The research of Chris Cornelis is funded by the Odysseus programme of the Science Foundation-Flanders (FWO).
Publisher Copyright:
© 2019 Elsevier Inc.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Fuzzy rough set theory models both vagueness and indiscernibility in data, which makes it a very useful tool for application to various machine learning tasks. In this paper, we focus on one of its robust generalisations, namely ordered weighted average based fuzzy rough sets. This model uses a weighted approach in the definition of the fuzzy rough operators. Although its efficacy and competitiveness with state-of-the-art machine learning approaches has been well established in several studies, its main drawback is the difficulty in choosing an appropriate weighting scheme. Several options exist and an adequate choice can greatly enhance the suitability of the ordered weighted average based fuzzy rough operators. In this work, we develop a clear strategy for the weighting scheme selection based upon the underlying characteristics of the data. The advantages of the approach are presented in a detailed experimental study focusing. Rather than to propose a classifier, our aim is to present a strategy to select a suitable weighting scheme for ordered weighted average based fuzzy rough sets in general. Our weighting scheme selection process allows users to take full advantage of the versatility offered by this model and performance improvements over the traditional fuzzy rough set approaches
AB - Fuzzy rough set theory models both vagueness and indiscernibility in data, which makes it a very useful tool for application to various machine learning tasks. In this paper, we focus on one of its robust generalisations, namely ordered weighted average based fuzzy rough sets. This model uses a weighted approach in the definition of the fuzzy rough operators. Although its efficacy and competitiveness with state-of-the-art machine learning approaches has been well established in several studies, its main drawback is the difficulty in choosing an appropriate weighting scheme. Several options exist and an adequate choice can greatly enhance the suitability of the ordered weighted average based fuzzy rough operators. In this work, we develop a clear strategy for the weighting scheme selection based upon the underlying characteristics of the data. The advantages of the approach are presented in a detailed experimental study focusing. Rather than to propose a classifier, our aim is to present a strategy to select a suitable weighting scheme for ordered weighted average based fuzzy rough sets in general. Our weighting scheme selection process allows users to take full advantage of the versatility offered by this model and performance improvements over the traditional fuzzy rough set approaches
KW - Fuzzy rough set theory
KW - Meta-learning
KW - Ordered weighted average
UR - http://www.scopus.com/inward/record.url?scp=85066937829&partnerID=8YFLogxK
U2 - 10.1016/j.ins.2019.05.085
DO - 10.1016/j.ins.2019.05.085
M3 - Article
SN - 0020-0255
VL - 501
SP - 155
EP - 171
JO - Information Sciences
JF - Information Sciences
ER -
