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Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set

Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set

Han Ke
Copyright: © 2020 |Pages: 20
ISBN13: 9781799824602|ISBN10: 1799824608|EISBN13: 9781799824619
DOI: 10.4018/978-1-7998-2460-2.ch014
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MLA

Ke, Han. "Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set." Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, IGI Global, 2020, pp. 263-282. https://doi.org/10.4018/978-1-7998-2460-2.ch014

APA

Ke, H. (2020). Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set. In I. Management Association (Ed.), Cognitive Analytics: Concepts, Methodologies, Tools, and Applications (pp. 263-282). IGI Global. https://doi.org/10.4018/978-1-7998-2460-2.ch014

Chicago

Ke, Han. "Designing Extreme Learning Machine Network Structure Based on Tolerance Rough Set." In Cognitive Analytics: Concepts, Methodologies, Tools, and Applications, edited by Information Resources Management Association, 263-282. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-2460-2.ch014

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Abstract

In this paper, we present a new extreme learning machine network structure on the basis of tolerance rough set. The purpose of this paper is to realize the high-efficiency and multi-dimensional ELM network structure. Various published algorithms have been applied to breast cancer datasets, but rough set is a fairly new intelligent technique that applies to predict breast cancer recurrence. We analyze Ljubljana Breast Cancer Dataset, firstly, obtain lower and upper approximations and calculate the accuracy and quality of the classification. The high values of the quality of classification and accuracy prove that the attributes selected can well approximate the classification. Rough sets approach is established to solve the prolem of tolerance.

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