Optimization of Ensemble Classifier System based on Multiple Objectives Genetic Algorithm

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Author(s)
Tien, Thanh Nguyen
Liew, Alan Wee-Chung
Xuan, Cuong Pham
Mal, Phuong Nguyen
Griffith University Author(s)
Year published
2014
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This paper introduces a mechanism to learn optimal combining classifier algorithms associated with features and classifiers from an ensemble system. By using Genetic Algorithm approach that focuses on 3 objectives namely number of correct classified observations and number of selected features and classifiers, optimal solution can be achieved after several interactions of crossover and mutation. We also employ OWA operator in which a weight vector is built by Linear Decreasing (LD) function to average outputs from combining algorithms. Experiments on 2 well-known UCI Machine Learning Repository datasets demonstrate benefits ...
View more >This paper introduces a mechanism to learn optimal combining classifier algorithms associated with features and classifiers from an ensemble system. By using Genetic Algorithm approach that focuses on 3 objectives namely number of correct classified observations and number of selected features and classifiers, optimal solution can be achieved after several interactions of crossover and mutation. We also employ OWA operator in which a weight vector is built by Linear Decreasing (LD) function to average outputs from combining algorithms. Experiments on 2 well-known UCI Machine Learning Repository datasets demonstrate benefits of our approach compared with other state-of-art ensemble method like Decision Template and SCANN as well as all fixed combining algorithms in the ensemble system.
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View more >This paper introduces a mechanism to learn optimal combining classifier algorithms associated with features and classifiers from an ensemble system. By using Genetic Algorithm approach that focuses on 3 objectives namely number of correct classified observations and number of selected features and classifiers, optimal solution can be achieved after several interactions of crossover and mutation. We also employ OWA operator in which a weight vector is built by Linear Decreasing (LD) function to average outputs from combining algorithms. Experiments on 2 well-known UCI Machine Learning Repository datasets demonstrate benefits of our approach compared with other state-of-art ensemble method like Decision Template and SCANN as well as all fixed combining algorithms in the ensemble system.
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Conference Title
PROCEEDINGS OF 2014 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOL 1
Volume
1
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Copyright Statement
© 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Subject
Neural, Evolutionary and Fuzzy Computation