| Autor: |
Chitra, P. K. A., Balamurugan, S. Appavu alias, Geetha, S., Kadry, Seifedine, Kim, Jungeun, Han, Keejun |
| Zdroj: |
Computer Systems Science & Engineering; 2024, Vol. 48 Issue 5, p1367-1385, 19p |
| Abstrakt: |
A generalization of supervised single-label learning based on the assumption that each sample in a dataset may belong to more than one class simultaneously is called multi-label learning. The main objective of this work is to create a novel framework for learning and classifying imbalanced multi-label data. This work proposes a framework of two phases. The imbalanced distribution of the multi-label dataset is addressed through the proposed Borderline MLSMOTE resampling method in phase 1. Later, an adaptive weighted l 21norm regularized (Elastic-net) multi-label logistic regression is used to predict unseen samples in phase 2. The proposed Borderline MLSMOTE resampling method focuses on samples with concurrent high labels in contrast to conventional MLSMOTE. The minority labels in these samples are called difficult minority labels and are more prone to penalize classification performance. The concurrent measure is considered borderline, and labels associated with samples are regarded as borderline labels in the decision boundary. In phase II, a novel adaptive l 21norm regularized weighted multi-label logistic regression is used to handle balanced data with different weighted synthetic samples. Experimentation on various benchmark datasets shows the outperformance of the proposed method and its powerful predictive performances over existing conventional state-of-the-art multi-label methods. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Supplemental Index |
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