Multi-target classification: Methodology and practical case studies

Mark Last

doi:10.1007/978-3-319-46131-1_35

Multi-target classification: Methodology and practical case studies

Mark Last

Department of Software and Information Systems Engineering

Ben-Gurion University of the Negev

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Most classification algorithms are aimed at predicting the value or values of a single target (class) attribute. However, some real-world classification tasks involve several targets that need to be predicted simultaneously. The Multiobjective Info-Fuzzy Network (M-IFN) algorithm builds an ordered (oblivious) decision-tree model for a multi-target classification task. After summarizing the principles and the properties of the M-IFN algorithm, this paper reviews three case studies of applying M-IFN to practical problems in industry and science.

Original language	American English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
Editors	Björn Bringmann, Elisa Fromont, Nikolaj Tatti, Volker Tresp, Pauli Miettinen, Bettina Berendt, Gemma Garriga
Publisher	Springer Verlag
Pages	280-283
Number of pages	4
ISBN (Print)	9783319461304
DOIs	https://doi.org/10.1007/978-3-319-46131-1_35
State	Published - 1 Jan 2016
Event	15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016 - Riva del Garda, Italy Duration: 19 Sep 2016 → 23 Sep 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9853 LNCS

Conference

Conference	15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016
Country/Territory	Italy
City	Riva del Garda
Period	19/09/16 → 23/09/16

Keywords

Decision trees
Information theory
Multi-objective info-fuzzy networks
Multi-target classification

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-46131-1_35

Cite this

Last, M. (2016). Multi-target classification: Methodology and practical case studies. In B. Bringmann, E. Fromont, N. Tatti, V. Tresp, P. Miettinen, B. Berendt, & G. Garriga (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings (pp. 280-283). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9853 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-46131-1_35

Last, Mark. / Multi-target classification : Methodology and practical case studies. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings. editor / Björn Bringmann ; Elisa Fromont ; Nikolaj Tatti ; Volker Tresp ; Pauli Miettinen ; Bettina Berendt ; Gemma Garriga. Springer Verlag, 2016. pp. 280-283 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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Last, M 2016, Multi-target classification: Methodology and practical case studies. in B Bringmann, E Fromont, N Tatti, V Tresp, P Miettinen, B Berendt & G Garriga (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9853 LNCS, Springer Verlag, pp. 280-283, 15th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2016, Riva del Garda, Italy, 19/09/16. https://doi.org/10.1007/978-3-319-46131-1_35

Multi-target classification: Methodology and practical case studies. / Last, Mark.
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings. ed. / Björn Bringmann; Elisa Fromont; Nikolaj Tatti; Volker Tresp; Pauli Miettinen; Bettina Berendt; Gemma Garriga. Springer Verlag, 2016. p. 280-283 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9853 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Most classification algorithms are aimed at predicting the value or values of a single target (class) attribute. However, some real-world classification tasks involve several targets that need to be predicted simultaneously. The Multiobjective Info-Fuzzy Network (M-IFN) algorithm builds an ordered (oblivious) decision-tree model for a multi-target classification task. After summarizing the principles and the properties of the M-IFN algorithm, this paper reviews three case studies of applying M-IFN to practical problems in industry and science.

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BT - Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings

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Last M. Multi-target classification: Methodology and practical case studies. In Bringmann B, Fromont E, Tatti N, Tresp V, Miettinen P, Berendt B, Garriga G, editors, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings. Springer Verlag. 2016. p. 280-283. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-46131-1_35

Multi-target classification: Methodology and practical case studies

Abstract

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Conference

Keywords

All Science Journal Classification (ASJC) codes

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