Generally, data is available abundantly in unlabeled form, and its annotation requires some
cost. The labeling, as well as learning cost, can be minimized by learning with the minimum …

Class imbalance is an intrinsic characteristic of multi-label data. Most of the labels in multi-
label data sets are associated with a small number of training examples, much smaller …

Logistic regression is by far the most widely used classifier in real-world applications. In this
paper, we benchmark the state-of-the-art active learning methods for logistic regression and …

Due to technological advances, a massive amount of data is produced daily, presenting
challenges for application areas where data needs to be labelled by a domain specialist or …

In this paper, we present a multi-label active learning-based approach to handle the
problem of classification of commit messages. The approach will help developers track …

Abundant data with limited labeling are a widespread bottleneck in multilabel learning.
Active learning (AL) is an effective solution to gradually enhance model robustness …

Multi-label active learning (MLAL) reduces the cost of manual annotation for multi-label
problems by selecting high-quality unlabeled data. Existing MLAL methods usually perform …

Multi-label text classification (MLTC) allows a given text to be associated with multiple
labels, which well suits many real-world data mining scenarios. However, the annotation …

Abstract Version space, defined as the subset of the hypothesis space consistent with the
training samples, is an important concept in supervised learning. It has been successfully …

Given a set of labels, multi-label text classification (MLTC) aims to assign multiple relevant
labels for a text. Recently, deep learning models get inspiring results in MLTC. Training a …