Evaluating Clustering Meta-features for Classifier Recommendation

Luís Garcia*, Felipe Campelo, Guilherme Ramos, Adriano Rivolli, André de Carvalho

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


Data availability in a wide variety of domains has boosted the use of Machine Learning techniques for knowledge discovery and classification. The performance of a technique in a given classification task is significantly impacted by specific characteristics of the dataset, which makes the problem of choosing the most adequate approach a challenging one. Meta-Learning approaches, which learn from meta-features calculated from the dataset, have been successfully used to suggest the most suitable classification algorithms for specific datasets. This work proposes the adaptation of clustering measures based on internal indices for supervised problems as additional meta-features in the process of learning a recommendation system for classification tasks. The gains in performance due to Meta-Learning and the additional meta-features are investigated with experiments based on 400 datasets, representing diverse application contexts and domains. Results suggest that (i) meta-learning is a viable solution for recommending a classifier, (ii) the use of clustering features can contribute to the performance of the recommendation system, and (iii) the computational cost of Meta-Learning is substantially smaller than that of running all candidate classifiers in order to select the best.
Original languageEnglish
Title of host publicationIntelligent Systems - 10th Brazilian Conference, BRACIS 2021, Proceedings, Part 1
EditorsAndré Britto, Karina Valdivia Delgado
Number of pages15
ISBN (Electronic)978-3-030-91702-9
ISBN (Print)978-3-030-91701-2
Publication statusPublished - 28 Nov 2021
Event10th Brazilian Conference, BRACIS 2021 -
Duration: 29 Nov 20213 Dec 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13073 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th Brazilian Conference, BRACIS 2021


  • Characterization measures
  • Clustering problems
  • Meta-features
  • Meta-learning


Dive into the research topics of 'Evaluating Clustering Meta-features for Classifier Recommendation'. Together they form a unique fingerprint.

Cite this