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All Journal International Journal of Electrical and Computer Engineering
Visweswariah Susheela Devi
Indian Institute of Science
Computer Science & IT Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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Multi-label learning by extended multi-tier stacked ensemble method with label correlated feature subset augmentation Hemavati Hemavati; Visweswariah Susheela Devi; Ramalingappa Aparna
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3384-3397

Abstract

Classification is one of the basic and most important operations that can be used in data science and machine learning applications. Multi-label classification is an extension of the multi-class problem where a set of class labels are associated with a particular instance at a time. In a multiclass problem, a single class label is associated with an instance at a time. However, there are many different stacked ensemble methods that have been proposed and because of the complexity associated with the multi-label problems, there is still a lot of scope for improving the prediction accuracy. In this paper, we are proposing the novel extended multi-tier stacked ensemble (EMSTE) method with label correlationby feature subset selection technique and then augmenting those feature subsets while constructing the intermediate dataset for improving the prediction accuracy in the generalization phase of the stacking. The performance effect of the proposed method has been compared with existing methods and showed that our proposed method outperforms the other methods.
Co-Authors Hemavati Hemavati Ramalingappa Aparna