International Journal of Computational Intelligence Systems

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Volume 12, Issue 2, 2019, Pages 1602 - 1612

Subgroup Discovery on Multiple Instance Data

Authors
J. M. Luna1, 4, C. J. Carmona2, A. M. García-Vico2, M. J. del Jesus2, S. Ventura1, 3, 4, *
1Department of Computer Science and Numerical Analysis, University of Cordoba, Cordoba, Spain
2Andalusian Research Institute on Data Science and Computational Intelligence, University of Jaen, Jaen, Spain
3Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
4Knowledge Discovery and Intelligent Systems in Biomedicine Laboratory, Maimonides Biomedical Research Institute of Cordoba, Cordoba, Spain
*Corresponding author. Email: sventura@uco.es
Corresponding Author
S. Ventura
Received 18 February 2019, Accepted 5 August 2019, Available Online 19 December 2019.
DOI
10.2991/ijcis.d.191213.001How to use a DOI?
Keywords
Supervised descriptive patterns; Subgroup discovery; Multi-instance data; Metaheuristics
Abstract

To date, the subgroup discovery (SD) task has been considered in problems where a target variable is unequivocally described by a set of features, also known as instance. Nowadays, however, with the increasing interest in data storage, new data structures are being provided such as the multiple instance data in which a target variable value is ambiguously defined by a set of instances. Most of the proposals related to multiple instance data are based on predictive tasks and no supervised descriptive analysis can be provided when data is organized in this way. At this point, the aim of this work is to extend the SD task to cope with this type of data. SD is a really interesting task that aims at discovering interesting relationships between different features with respect to a specific target variable that is of interest for the user or the problem under study. In this regard, this paper presents three different approaches for mining interesting subgroups in multiple instance problems. The proposed models represent three different ways of tackling the problem and they are based on three well-known algorithms in the SD field: SD-Map (exhaustive search approach), CGBA-SD (Comprehensible Grammar-Based Algorithm for Subgroup Discovery) and NMEEF-SD (multi-objective evolutionary fuzzy system). The proposals have been tested on a wide set of datasets, including 10 real-world and 20 synthetic datasets, aiming at describing how the three methodologies behave on different scenarios. Any comparison is unfair since they are completely different methodologies.

Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 2
Pages
1602 - 1612
Publication Date
2019/12/19
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.d.191213.001How to use a DOI?
Copyright
© 2019 The Authors. Published by Atlantis Press SARL.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - J. M. Luna
AU  - C. J. Carmona
AU  - A. M. García-Vico
AU  - M. J. del Jesus
AU  - S. Ventura
PY  - 2019
DA  - 2019/12/19
TI  - Subgroup Discovery on Multiple Instance Data
JO  - International Journal of Computational Intelligence Systems
SP  - 1602
EP  - 1612
VL  - 12
IS  - 2
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.191213.001
DO  - 10.2991/ijcis.d.191213.001
ID  - Luna2019
ER  -