A Similarity-Based Oversampling Method for Multi-label Imbalanced Text Data

TL;DR

This paper presents a novel similarity-based oversampling technique for multi-label imbalanced text data, which improves classifier performance by intelligently augmenting the training set with similar unlabeled instances.

Contribution

It introduces a new oversampling method that leverages similarity measures to select unlabeled instances, addressing class imbalance in multi-label text classification.

Findings

Enhanced classifier performance after oversampling

Effective identification of beneficial unlabeled instances

Improved handling of class imbalance in multi-label data

Abstract

In real-world applications, as data availability increases, obtaining labeled data for machine learning (ML) projects remains challenging due to the high costs and intensive efforts required for data annotation. Many ML projects, particularly those focused on multi-label classification, also grapple with data imbalance issues, where certain classes may lack sufficient data to train effective classifiers. This study introduces and examines a novel oversampling method for multi-label text classification, designed to address performance challenges associated with data imbalance. The proposed method identifies potential new samples from unlabeled data by leveraging similarity measures between instances. By iteratively searching the unlabeled dataset, the method locates instances similar to those in underrepresented classes and evaluates their contribution to classifier performance enhancement. Instances that demonstrate performance improvement are then added to the labeled dataset. Experimental results indicate that the proposed approach effectively enhances classifier performance post-oversampling.