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研究生： 南宮濤
Dinar Nugroho Pratomo
論文名稱： Optimized Extreme Learning Machine using Fruit Fly Optimization for Classification
Optimized Extreme Learning Machine using Fruit Fly Optimization for Classification
指導教授： 呂永和
Yungho Leu
口試委員: 楊維寧
Wei-Ning Yang
陳雲岫
Yun-Shiow Chen
Agus Harjoko
Agus Harjoko
Suprapto
Suprapto
Agus Sihabuddin
Agus Sihabuddin
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理系
Department of Information Management
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 71
中文關鍵詞： Extreme learning machine 、fruit fly optimization algorithm 、classification
外文關鍵詞： Extreme learning machine, fruit fly optimization algorithm, classification
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    • Extreme Learning Machine (ELM) is one of the learning methods of Artificial Neural Network, which has many advantages, such as fast learning speed, good generalization performance and high accuracy results. However, one of the weaknesses of the ELM method is that the number of hidden nodes cannot be specified without applying the trial and error manually. It is not possible to obtain the right number of hidden nodes in order to get the good result in ELM Method. In this study we propose to use a new swarm intelligence algorithm, the Fruit Fly Optimization Algorithm (FOA), to optimize ELM and to find the optimal number of hidden nodes, weights, and biases. We tested the performance of the proposed method on several datasets from the UCI repository for classification. This research compares the proposed method with other optimization algorithms. The experimental result shows that optimizing ELM by FOA results in higher accuracy compared to other methods such as PSO-ELM, E-ELM, OP-ELM, VPSO-ELM, IPSO-ELM and the original ELM.

    Extreme Learning Machine (ELM) is one of the learning methods of Artificial Neural Network, which has many advantages, such as fast learning speed, good generalization performance and high accuracy results. However, one of the weaknesses of the ELM method is that the number of hidden nodes cannot be specified without applying the trial and error manually. It is not possible to obtain the right number of hidden nodes in order to get the good result in ELM Method. In this study we propose to use a new swarm intelligence algorithm, the Fruit Fly Optimization Algorithm (FOA), to optimize ELM and to find the optimal number of hidden nodes, weights, and biases. We tested the performance of the proposed method on several datasets from the UCI repository for classification. This research compares the proposed method with other optimization algorithms. The experimental result shows that optimizing ELM by FOA results in higher accuracy compared to other methods such as PSO-ELM, E-ELM, OP-ELM, VPSO-ELM, IPSO-ELM and the original ELM.

    ABSTRACT i ACKNOWLEDGEMENT ii TABLE OF CONTENTS iii LIST OF FIGURES iv LIST OF TABLES v Chapter 1. Introduction 1 1.1. Motivation 1 1.2 Research Problem 4 1.3 Research Objective 5 1.3.1 General Objective 5 1.3.2 Specific Objectives 5 1.4 Thesis Overview 5 1.4.1 Chapter 2 Problem Definitions 5 1.4.2 Chapter 3 Proposed Method 6 1.4.3 Chapter 4 Experimental Result 6 1.4.4 Chapter 5 Conclusion and Future Work 6 Chapter 2. Problem Definition 7 2.1 Neural Network 7 2.2 Single-Hidden Layer Feedforward Neural Networks (SLFNs) 9 2.3 Extreme Learning Machine (ELM) 11 2.4 Fruit Fly Optimization Algorithm 15 2.5 Classification 18 2.6 Classification Performance Measure 18 Chapter 3. Optimizing Extreme Learning Machine using Fruit Fly Optimization Algorithm 22 3.1. Datasets 22 3.2. FOA-ELM 26 3.2.1 FOA-ELM optimizing Number of Hidden Nodes 27 3.2.2 FOA-ELM optimizing values of the weights and the biases 29 3.2.3 FOA-ELM optimizing number of hidden nodes, values of the weights and the biases 32 Chapter 4. Experimental Result 35 4.1. Benchmark Datasets 35 4.1.1. Binary Class Datasets 35 4.1.2. Multiclass Datasets 36 4.2. Simulation Environment Settings 36 4.3. Experimental setup 37 4.4. Performance Comparison of Real-World Benchmark Datasets 38 Chapter 5. Conclusions and Future Research 48 5.1. Result Summary 48 5.2. Limitation 49 5.3. Future Research 49 REFERENCES 50

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