Towards a more efficient and cost-sensitive extreme learning machine: A state-of-the-art review of recent trend

Alaba, Peter Adeniyi and Popoola, Segun I. and Olatomiwa, Lanre and Alex, Opeoluwa David and Atayero, A. A. and Daud, Wan Mohd Wan (2019) Towards a more efficient and cost-sensitive extreme learning machine: A state-of-the-art review of recent trend. Neurocomputing. pp. 70-90.

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Abstract

In spite of the prominence of extreme learning machine model, as well as its excellent features such as insignificant intervention for learning and model tuning, the simplicity of implementation, and high learning speed, which makes it a fascinating alternative method for Artificial Intelligence, including Big Data Analytics, it is still limited in certain aspects. These aspects must be treated to achieve an effective and cost-sensitive model. This review discussed the major drawbacks of ELM, which include difficulty in determination of hidden layer structure, prediction instability and Imbalanced data distributions, the poor capability of sample structure preserving (SSP), and difficulty in accommodating lateral inhibition by direct random feature mapping. Other drawbacks include multi-graph complexity, global memory size, one-by-one or chuck-by-chuck (a block of data), global memory size limitation, and challenges with big data. The recent trend proposed by experts for each drawback is discussed in detail towards achieving an effective and cost-sensitive model

Item Type:	Article
Uncontrolled Keywords:	Extreme learning machine Artificial intelligence Big data analytics Sample structure preserving Imbalance data
Subjects:	T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TP Chemical technology
Divisions:	Faculty of Engineering, Science and Mathematics > School of Engineering Sciences
Depositing User:	Mrs Patricia Nwokealisi
Date Deposited:	14 Apr 2021 12:06
Last Modified:	14 Apr 2021 12:06
URI:	http://eprints.covenantuniversity.edu.ng/id/eprint/13983

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