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A Hybrid Feature Selection Algorithm Based on a Discrete Artificial Bee Colony for Parkinson's Diagnosis

Published: 09 June 2021 Publication History

Abstract

Parkinson's disease is a neurodegenerative disease that affects millions of people around the world and cannot be cured fundamentally. Automatic identification of early Parkinson's disease on feature data sets is one of the most challenging medical tasks today. Many features in these datasets are useless or suffering from problems like noise, which affect the learning process and increase the computational burden. To ensure the optimal classification performance, this article proposes a hybrid feature selection algorithm based on an improved discrete artificial bee colony algorithm to improve the efficiency of feature selection. The algorithm combines the advantages of filters and wrappers to eliminate most of the uncorrelated or noisy features and determine the optimal subset of features. In the filter, three different variable ranking methods are employed to pre-rank the candidate features, then the population of artificial bee colony is initialized based on the significance degree of the re-rank features. In the wrapper part, the artificial bee colony algorithm evaluates individuals (feature subsets) based on the classification accuracy of the classifier to achieve the optimal feature subset. In addition, for the first time, we introduce a strategy that can automatically select the best classifier in the search framework more quickly. By comparing with several publicly available datasets, the proposed method achieves better performance than other state-of-the-art algorithms and can extract fewer effective features.

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      Published In

      cover image ACM Transactions on Internet Technology
      ACM Transactions on Internet Technology  Volume 21, Issue 3
      August 2021
      522 pages
      ISSN:1533-5399
      EISSN:1557-6051
      DOI:10.1145/3468071
      • Editor:
      • Ling Liu
      Issue’s Table of Contents
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Publication History

      Published: 09 June 2021
      Online AM: 07 May 2020
      Accepted: 01 April 2020
      Revised: 01 March 2020
      Received: 01 February 2020
      Published in TOIT Volume 21, Issue 3

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      Author Tags

      1. Parkinson's disease
      2. machine learning
      3. feature extraction
      4. artificial bee colony algorithm

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      • Research-article
      • Refereed

      Funding Sources

      • National Nature Science Foundation of China
      • Beijing Natural Science Foundation
      • Research Committee of University of Macau
      • Science and Technology on Space Intelligent Control Laboratory
      • Science and Technology Development Fund of Macau SAR
      • Postgraduate Research & Practice Innovation Program of Jiangsu Province

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      • (2025)Improving Detection of Parkinson’s Disease with Acoustic Feature Optimization Using Particle Swarm Optimization and Machine LearningMachine Learning: Science and Technology10.1088/2632-2153/adadc3Online publication date: 23-Jan-2025
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      • (2024)Parkinson’s Disease Prediction Using Machine Learning and Nature-Inspired Optimization TechniqueProceedings of Fifth Doctoral Symposium on Computational Intelligence10.1007/978-981-97-6036-7_47(577-591)Online publication date: 4-Oct-2024
      • (2023)Evolving LSTM Networks for Time-Series Classification in EdgeIoTMathematical Problems in Engineering10.1155/2023/64690302023(1-10)Online publication date: 14-Apr-2023
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