# Explaining the Performance of Multi-label Classification Methods with Data Set Properties

arXiv: https://arxiv.org/abs/2106.15411 · HTML: https://mlsanity.com/paper/2106.15411/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 130 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [SimplE Embedding for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1802.04868/) (2018)
- [Hyperparameter Importance Across Datasets](https://mlsanity.com/paper/1710.04725/) (2017)
- [Ensemble Methods for Multi-label Classification](https://mlsanity.com/paper/1307.1769/) (2013)
- [Deep Learning for Multi-label Classification](https://mlsanity.com/paper/1502.05988/) (2014)
- [Cost-Sensitive Label Embedding for Multi-Label Classification](https://mlsanity.com/paper/1603.09048/) (2016)
- [Better Trees: An empirical study on hyperparameter tuning of classification decision tree induction algorithms](https://mlsanity.com/paper/1812.02207/) (2018)
- [Automated Multi-Label Classification based on ML-Plan](https://mlsanity.com/paper/1811.04060/) (2018)
- [Multi-label classification search space in the MEKA software](https://mlsanity.com/paper/1811.11353/) (2018)

## Built on by (most cited first, 3 of 3)

- [Explainable Model-specific Algorithm Selection for Multi-Label Classification](https://mlsanity.com/paper/2211.11227/) (2022)
- [FAIRification of MLC data](https://mlsanity.com/paper/2211.12757/) (2022)
- [Design Choices That Matter: A Functional ANOVA Analysis for Remote Sensing Multi-Label Classification](https://mlsanity.com/paper/2608.04702/) (2026)

## Abstract (arXiv)

Meta learning generalizes the empirical experience with different learning tasks and holds promise for providing important empirical insight into the behaviour of machine learning algorithms. In this paper, we present a comprehensive meta-learning study of data sets and methods for multi-label classification (MLC). MLC is a practically relevant machine learning task where each example is labelled with multiple labels simultaneously. Here, we analyze 40 MLC data sets by using 50 meta features describing different properties of the data. The main findings of this study are as follows. First, the most prominent meta features that describe the space of MLC data sets are the ones assessing different aspects of the label space. Second, the meta models show that the most important meta features describe the label space, and, the meta features describing the relationships among the labels tend to occur a bit more often than the meta features describing the distributions between and within the individual labels. Third, the optimization of the hyperparameters can improve the predictive performance, however, quite often the extent of the improvements does not always justify the resource utilization.