Advanced search
Start date
Betweenand
Related content
(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

An empirical analysis of binary transformation strategies and base algorithms for multi-label learning

Full text
Author(s):
Rivolli, Adriano [1] ; Read, Jesse [2] ; Soares, Carlos [3, 4] ; Pfahringer, Bernhard [5] ; de Carvalho, Andre C. P. L. F. [6, 7]
Total Authors: 5
Affiliation:
[1] Technol Univ Parana, Dept Comp Sci, Cornelio Procopio, Parana - Brazil
[2] Ecole Polytech, Lab Informat LIX, Palaiseau - France
[3] Univ Porto, Fraunhofer AICOS, Porto - Portugal
[4] Univ Porto, LIAAD INESC TEC, Porto - Portugal
[5] Univ Waikato, Hamilton - New Zealand
[6] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
[7] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP, Brazil.Rivolli, Adriano, Technol Univ Parana, Dept Comp Sci, Cornelio Procopio, Parana - Brazil
Total Affiliations: 7
Document type: Journal article
Source: MACHINE LEARNING; v. 109, n. 8 JUN 2020.
Web of Science Citations: 0
Abstract

Investigating strategies that are able to efficiently deal with multi-label classification tasks is a current research topic in machine learning. Many methods have been proposed, making the selection of the most suitable strategy a challenging issue. From this premise, this paper presents an extensive empirical analysis of the binary transformation strategies and base algorithms for multi-label learning. This subset of strategies uses the one-versus-all approach to transform the original data, generating one binary data set per label, upon which any binary base algorithm can be applied. Considering that the influence of the base algorithm on the predictive performance obtained by the strategies has not been considered in depth by many empirical studies, we investigated the influence of distinct base algorithms on the performance of several strategies. Thus, this study covers a family of multi-label strategies using a diversified range of base algorithms, exploring their relationship over different perspectives. This finding has significant implications concerning the methodology of evaluation adopted in multi-label experiments containing binary transformation strategies, given that multiple base algorithms should be considered. Despite these improvements in strategy and base algorithms, for many data sets, a large number of labels, mainly those less frequent, were either never predicted, or always misclassified. We conclude the experimental analysis by recommending strategies and base algorithms in accordance with different performance criteria. (AU)

FAPESP's process: 16/18615-0 - Advanced machine learning
Grantee:André Carlos Ponce de Leon Ferreira de Carvalho
Support Opportunities: Research Grants - Research Partnership for Technological Innovation - PITE
FAPESP's process: 13/07375-0 - CeMEAI - Center for Mathematical Sciences Applied to Industry
Grantee:Francisco Louzada Neto
Support Opportunities: Research Grants - Research, Innovation and Dissemination Centers - RIDC
FAPESP's process: 12/22608-8 - Use of data complexity measures in the support of supervised machine learning
Grantee:Ana Carolina Lorena
Support Opportunities: Research Grants - Young Investigators Grants