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.

Details

Title
Multi layered Stacked Ensemble Method with Feature Reduction Technique for Multi-Label Classification
Author
V Susheela Devi 1 ; Aparna, R 2 

 Department of CSA, Indian Institute of Scence , Bangaluru, Karnataka , India 
 Department of CSE, Siddaganga Institute of Technology , Tumakuru, Karnataka , India 
First page
012074
Publication year
2022
Publication date
Jan 2022
Publisher
IOP Publishing
ISSN
17426588
e-ISSN
17426596
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2635867408
Copyright
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.