# Applying an Ensemble Learning Method for Improving Multi-label Classification Performance

arXiv: https://arxiv.org/abs/1801.02149 · HTML: https://mlsanity.com/paper/1801.02149/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jan 2018.

## Built on by (most cited first, 1 of 1)

- [Employing Iterative Feature Selection in Fuzzy Rule-Based Binary Classification](https://mlsanity.com/paper/2401.16244/) (2024)

## Abstract (arXiv)

In recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algorithms in which an operator takes a number of learning algorithms, namely base-level algorithms and combines their outcomes to make an estimation. The simplest form of ensemble learning is to train the base-level algorithms on random subsets of data and then let them vote for the most popular classifications or average the predictions of the base-level algorithms. In this study, an ensemble learning method is proposed for improving multi-label classification evaluation criteria. We have compared our method with well-known base-level algorithms on some data sets. Experiment results show the proposed approach outperforms the base well-known classifiers for the multi-label classification problem.