A Robust Experimental Evaluation of Automated Multi-Label Classification Methods

TL;DR

This paper evaluates various automated multi-label classification methods, highlighting the effectiveness of a grammar-based genetic programming approach over other AutoML techniques across multiple datasets.

Contribution

It provides a comprehensive comparison of five AutoML methods for multi-label classification, introducing Auto-MEKA_GGP as a superior approach based on extensive experiments.

Findings

Auto-MEKA_GGP achieved the best average results.

Auto-MEKA_GGP was statistically better than other methods in most cases.

The study highlights the effectiveness of grammar-based genetic programming for MLC AutoML.

Abstract

Automated Machine Learning (AutoML) has emerged to deal with the selection and configuration of algorithms for a given learning task. With the progression of AutoML, several effective methods were introduced, especially for traditional classification and regression problems. Apart from the AutoML success, several issues remain open. One issue, in particular, is the lack of ability of AutoML methods to deal with different types of data. Based on this scenario, this paper approaches AutoML for multi-label classification (MLC) problems. In MLC, each example can be simultaneously associated to several class labels, unlike the standard classification task, where an example is associated to just one class label. In this work, we provide a general comparison of five automated multi-label classification methods -- two evolutionary methods, one Bayesian optimization method, one random search and one greedy search -- on 14 datasets and three designed search spaces. Overall, we observe that the most prominent method is the one based on a canonical grammar-based genetic programming (GGP) search method, namely Auto-MEKA$_{GGP}$. Auto-MEKA$_{GGP}$ presented the best average results in our comparison and was statistically better than all the other methods in different search spaces and evaluated measures, except when compared to the greedy search method.