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Document details - Optimizing Extreme Learning Machine using Whale Optimization Algorithm for Genes Classification

Journal Volume 10, Issue 1, January - February 2021, Article 9812213 Mustafa Abdul Salam, Ahmed Taher Azar, Rana Hussien , " Optimizing Extreme Learning Machine using Whale Optimization Algorithm for Genes Classification " , International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) , Volume 10, Issue 1, January - February 2021 , pp. 023-031 , ISSN 2278 - 6856.

Optimizing Extreme Learning Machine using Whale Optimization Algorithm for Genes Classification

    Mustafa Abdul Salam, Ahmed Taher Azar, Rana Hussien

Abstract

Abstract:This paper presents the hybridization of Whale Optimization Algorithm (WOA) with Extreme Learning Machine (ELM) methodology for solving Gene classification problem. ELM is assumed to be a likely technique for prediction and classification problems. Despite its effectiveness, it needs a large number of nodes on a regular basis for the hidden layer. Using such a huge number of nodes within the hidden layer increases the ELM examination/assessment time. In addition, there is a little guarantee that the layout of weights and biases inside the hidden layer would be optimum. A recent swarm intelligence algorithm (WOA) mimics the conduct of the hunting party of humpback whales is proposed to optimize the ELM model. It is being used within the hidden layer to pick a smaller number of nodes to accelerate the execution of ELM. WOA chooses the optimal weights and bias of the hidden layer. Experimental results show that the proposed hybrid model (WOA-ELM) had better classification accuracy than the standard ELM and SVM. Keywords:Whale Optimization Algorithm, Bio-inspired Optimization, Extreme Learning Machine

  • ISSN: 22786856
  • Source Type: Journal
  • Original language: English

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