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Fortified Offspring Fuzzy Neural Networks Algorithm

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Book cover Soft Computing in Data Science (SCDS 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 937))

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Abstract

Our research here suggests a fortified Offspring fuzzy neural networks (FOFNN) classifier developed with the aid of Fuzzy C-Means (FCM). The objective of this study concerns the selection of preprocessing techniques for the dimensionality reduction of input space. Principal component analysis (PCA) algorithm presents a pre-processing phase to the network to shape the low-dimensional input variables. Subsequently, the effectual step to handle uncertain information by type-2 fuzzy sets using Fuzzy C-Means (FCM) clustering. The proposition (condition) phase of the rules is formed by two FCM clustering algorithms, which are appealed by spending distinct values of the fuzzification coefficient successively resulting in valued type-2 membership functions. The simultaneous parametric optimization of the network by the evolutionary algorithm is finalized. The suggested classifier is applied to some machine learning datasets, and the results are compared with those provided by other classifiers reported in the literature.

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Authors and Affiliations

  1. Higher Colleges of Technology, Abu Dhabi, Al Ain, UAE

    Kefaya Qaddoum

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  1. Kefaya Qaddoum
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Correspondence to Kefaya Qaddoum .

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Editors and Affiliations

  1. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Bee Wah Yap

  2. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

    Azlinah Hj Mohamed

  3. Department of Electrical Engineering and Computer Science, University of Tennessee at Knoxville, Knoxville, TN, USA

    Michael W. Berry

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Qaddoum, K. (2019). Fortified Offspring Fuzzy Neural Networks Algorithm. In: Yap, B., Mohamed, A., Berry, M. (eds) Soft Computing in Data Science. SCDS 2018. Communications in Computer and Information Science, vol 937. Springer, Singapore. https://doi.org/10.1007/978-981-13-3441-2_14

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  • DOI: https://doi.org/10.1007/978-981-13-3441-2_14

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-3440-5

  • Online ISBN: 978-981-13-3441-2

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