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Feature selection using binary particle swarm optimization with time varying inertia weight strategies

Published: 26 June 2018 Publication History

Abstract

In this paper, a feature selection approach that based on Binary Particle Swarm Optimization (PSO) with time varying inertia weight strategies is proposed. Feature Selection is an important preprocessing technique that aims to enhance the learning algorithm (e.g., classification) by improving its performance or reducing the processing time or both of them. Searching for the best feature set is a challenging problem in feature selection process, metaheuristics algorithms have proved a good performance in finding the (near) optimal solution for this problem. PSO algorithm is considered a primary Swarm Intelligence technique that showed a good performance in solving different optimization problems. A key component that highly affect the performance of PSO is the updating strategy of the inertia weight that controls the balance between exploration and exploitation. This paper studies the effect of different time varying inertia weight updating strategies on the performance of BPSO in tackling feature selection problem. To assess the performance of the proposed approach, 18 standard UCI datasets were used. The proposed approach is compared with well regarded metaheuristics based feature selection approaches, and the results proved the superiority of the proposed approach.

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    cover image ACM Other conferences
    ICFNDS '18: Proceedings of the 2nd International Conference on Future Networks and Distributed Systems
    June 2018
    469 pages
    ISBN:9781450364287
    DOI:10.1145/3231053
    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: 26 June 2018

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    Author Tags

    1. PSO
    2. binary particle swarm optimization
    3. classification
    4. feature selection
    5. inertia weight
    6. optimization

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    • (2024)Learning optimal deep prototypes for video retrieval systems with hybrid SVM-softmax layerInternational Journal of Data Science and Analytics10.1007/s41060-024-00587-wOnline publication date: 18-Jun-2024
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