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Particle swarm optimization for multi-label classification

Authors:

Tiago Amador Coelho,

Ahmed Ali Abdala Esmin,

Wagner Meira JúniorAuthors Info & Claims

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

Pages 17 - 18

https://doi.org/10.1145/2001858.2001868

Published: 12 July 2011 Publication History

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Abstract

Multi-label classification learning first arose in the context of text categorization, where each document may belong to several classes simultaneously and has attracted significant attention lately, as a consequence of both the challenge it represents and its relevance in terms of application scenarios. In this paper, we propose a new hybrid approach, Multi Label K-Nearest Michigan Particle Swarm Optimization (ML-KMPSO), that is based on two strategies: Michigan Particle Swarm Optimization (MPSO) and ML-KNN. We evaluated the performance of ML-KMPSO using two real-world datasets and the results show that our proposal matches or outperforms well-established multi-label classification learning algorithms.

References

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A. Cervantes, I. M. Galván, and P. I. Viñuela. Michigan particle swarm optimization for prototype reduction in classification problems. New Generation Comput., 27(3):239--257, 2009.

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A. A. A. Esmin. Generating fuzzy rules from examples using the particle swarm optimization algorithm. In HIS, pages 340--343, 2007.

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M.-L. Zhang and Z.-H. Zhou. ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognition, 40(7):2038--2048, July 2007.

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Cited By

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Agrawal SAgrawal JKaur SSharma S(2018)A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classificationNeural Computing and Applications10.5555/3184485.318450729:1(245-256)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.5555/3184485.3184507
Agrawal JAgrawal SKaur SSharma S(2014)An Investigation of Fuzzy PSO and Fuzzy SVD Based RBF Neural Network for Multi-label ClassificationProceedings of the Third International Conference on Soft Computing for Problem Solving10.1007/978-81-322-1771-8_59(677-687)Online publication date: 4-Mar-2014
https://doi.org/10.1007/978-81-322-1771-8_59

Particle swarm optimization for multi-label classification
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

GECCO '11: Proceedings of the 13th annual conference companion on Genetic and evolutionary computation

July 2011

1548 pages

ISBN:9781450306904

DOI:10.1145/2001858

Editor:
Natalio Krasnogor
University of Nottingham, UK
,
General Chair:
Pier Luca Lanzi
Politecnico di Milano, Italy

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Association for Computing Machinery

New York, NY, United States

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Published: 12 July 2011

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SIGEVO

GECCO '11: Genetic and Evolutionary Computation Conference

July 12 - 16, 2011

Dublin, Ireland

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Agrawal SAgrawal JKaur SSharma S(2018)A comparative study of fuzzy PSO and fuzzy SVD-based RBF neural network for multi-label classificationNeural Computing and Applications10.5555/3184485.318450729:1(245-256)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.5555/3184485.3184507
Agrawal JAgrawal SKaur SSharma S(2014)An Investigation of Fuzzy PSO and Fuzzy SVD Based RBF Neural Network for Multi-label ClassificationProceedings of the Third International Conference on Soft Computing for Problem Solving10.1007/978-81-322-1771-8_59(677-687)Online publication date: 4-Mar-2014
https://doi.org/10.1007/978-81-322-1771-8_59

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Enhanced particle swarm optimization with multi-swarm and multi-velocity for optimizing high-dimensional problems

An improved cooperative quantum-behaved particle swarm optimization

An enhanced particle swarm optimization with levy flight for global optimization

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