Applying an Ensemble Learning Method for Improving Multi-label Classification Performance
Abstract
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.
Cite
@article{arxiv.1801.02149,
title = {Applying an Ensemble Learning Method for Improving Multi-label Classification Performance},
author = {Amirreza Mahdavi-Shahri and Mahboobeh Houshmand and Mahdi Yaghoobi and Mehrdad Jalali},
journal= {arXiv preprint arXiv:1801.02149},
year = {2018}
}