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Weight selection strategies for ordered weighted average based fuzzy rough sets

(2019) INFORMATION SCIENCES. 501. p.155-171
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Abstract
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. (C) 2019 Elsevier Inc. All rights reserved.
Keywords
AGGREGATION OPERATORS, IMBALANCED DATA, CLASSIFICATION, Fuzzy rough set theory, Ordered weighted average, Meta-learning

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Please use this url to cite or link to this publication:

MLA
Vluymans, Sarah et al. “Weight Selection Strategies for Ordered Weighted Average Based Fuzzy Rough Sets.” INFORMATION SCIENCES 501 (2019): 155–171. Print.
APA
Vluymans, S., Mac Parthalain, N., Cornelis, C., & Saeys, Y. (2019). Weight selection strategies for ordered weighted average based fuzzy rough sets. INFORMATION SCIENCES, 501, 155–171.
Chicago author-date
Vluymans, Sarah, Neil Mac Parthalain, Chris Cornelis, and Yvan Saeys. 2019. “Weight Selection Strategies for Ordered Weighted Average Based Fuzzy Rough Sets.” Information Sciences 501: 155–171.
Chicago author-date (all authors)
Vluymans, Sarah, Neil Mac Parthalain, Chris Cornelis, and Yvan Saeys. 2019. “Weight Selection Strategies for Ordered Weighted Average Based Fuzzy Rough Sets.” Information Sciences 501: 155–171.
Vancouver
1.
Vluymans S, Mac Parthalain N, Cornelis C, Saeys Y. Weight selection strategies for ordered weighted average based fuzzy rough sets. INFORMATION SCIENCES. New york: Elsevier Science Inc; 2019;501:155–71.
IEEE
[1]
S. Vluymans, N. Mac Parthalain, C. Cornelis, and Y. Saeys, “Weight selection strategies for ordered weighted average based fuzzy rough sets,” INFORMATION SCIENCES, vol. 501, pp. 155–171, 2019.
@article{8628742,
  abstract     = {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. (C) 2019 Elsevier Inc. All rights reserved.},
  author       = {Vluymans, Sarah and Mac Parthalain, Neil and Cornelis, Chris and Saeys, Yvan},
  issn         = {0020-0255},
  journal      = {INFORMATION SCIENCES},
  keywords     = {AGGREGATION OPERATORS,IMBALANCED DATA,CLASSIFICATION,Fuzzy rough set theory,Ordered weighted average,Meta-learning},
  language     = {eng},
  pages        = {155--171},
  publisher    = {Elsevier Science Inc},
  title        = {Weight selection strategies for ordered weighted average based fuzzy rough sets},
  url          = {http://dx.doi.org/10.1016/j.ins.2019.05.085},
  volume       = {501},
  year         = {2019},
}

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