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Multi layered Stacked Ensemble Method with Feature Reduction Technique for Multi-Label Classification

Journal of physics. Conference series, 2022-01, Vol.2161 (1), p.12074 [Peer Reviewed Journal]

Published under licence by IOP Publishing Ltd ;Published under licence by IOP Publishing Ltd. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1742-6588 ;EISSN: 1742-6596 ;DOI: 10.1088/1742-6596/2161/1/012074

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  • Title:
    Multi layered Stacked Ensemble Method with Feature Reduction Technique for Multi-Label Classification
  • Author: Hemavati ; Susheela Devi, V ; Aparna, R
  • Subjects: Classification ; Classifiers ; Principal components analysis ; Reduction
  • Is Part Of: Journal of physics. Conference series, 2022-01, Vol.2161 (1), p.12074
  • Description: Abstract Nowadays, multi-label classification can be considered as one of the important challenges for classification problem. In this case instances are assigned more than one class label. Ensemble learning is a process of supervised learning where several classifiers are trained to get a better solution for a given problem. Feature reduction can be used to improve the classification accuracy by considering the class label information with principal Component Analysis (PCA). In this paper, stacked ensemble learning method with augmented class information PCA (CA PCA) is proposed for classification of multi-label data (SEMML). In the initial step, the dimensionality reduction step is applied, then the number of classifiers have to be chosen to apply on the original training dataset, then the stacking method is applied to it. By observing the results of experiments conducted are showing our proposed method is working better as compared to the existing methods.
  • Publisher: Bristol: IOP Publishing
  • Language: English
  • Identifier: ISSN: 1742-6588
    EISSN: 1742-6596
    DOI: 10.1088/1742-6596/2161/1/012074
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    IOPscience (Open Access)
    Institute of Physics Open Access Journal Titles
    ProQuest Central
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