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

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

3 Scopus citations

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	English
Title of host publication	Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings
Editors	Bettina Berendt, Björn Bringmann, Elisa Fromont, Gemma Garriga, Pauli Miettinen, Nikolaj Tatti, Volker Tresp
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
Volume	9853 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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

ASJC Scopus subject areas

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. Berendt, B. Bringmann, E. Fromont, G. Garriga, P. Miettinen, N. Tatti, & V. Tresp (Eds.), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings (pp. 280-283). (Lecture Notes in Computer Science; 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 / Bettina Berendt ; Björn Bringmann ; Elisa Fromont ; Gemma Garriga ; Pauli Miettinen ; Nikolaj Tatti ; Volker Tresp. Springer Verlag, 2016. pp. 280-283 (Lecture Notes in Computer Science).

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Last, M 2016, Multi-target classification: Methodology and practical case studies. in B Berendt, B Bringmann, E Fromont, G Garriga, P Miettinen, N Tatti & V Tresp (eds), Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2016, Proceedings. Lecture Notes in Computer Science, 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. / Bettina Berendt; Björn Bringmann; Elisa Fromont; Gemma Garriga; Pauli Miettinen; Nikolaj Tatti; Volker Tresp. Springer Verlag, 2016. p. 280-283 (Lecture Notes in Computer Science; 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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Last M. Multi-target classification: Methodology and practical case studies. In Berendt B, Bringmann B, Fromont E, Garriga G, Miettinen P, Tatti N, Tresp V, 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). doi: 10.1007/978-3-319-46131-1_35

Multi-target classification: Methodology and practical case studies

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Keywords

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