International Journal of Computational Intelligence Systems

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Volume 9, Issue 1, January 2016, Pages 184 - 201

Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms

Authors
Javier García1, jgf00002@red.ujaen.es, Adnan M. AlBar2, ambar@kau.edu.sa, Naif R. Aljohani2, nraljohani@kau.edu.sa, José-Ramón Cano1, jrcano@ujaen.es, Salvador García3, salvagl@decsai.ugr.es
1Department of Computer Science, University of Jaén, EPS of Linares, Calle Alfonso X el Sabio S/N, Linares, 23700, Spain
2Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia
3Department of Computer Science and Artificial Intelligence, University of Granada, Calle Periodista Daniel Saucedo Aranda S/N, Granada, 18071, Spain
Received 4 July 2015, Accepted 3 January 2016, Available Online 20 January 2016.
DOI
10.1080/18756891.2016.1146536How to use a DOI?
Keywords
Monotonic Classification; Nested Generalized Examples; Evolutionary Algorithms; Rule Induction; Instance-based Learning
Abstract

In supervised learning, some real problems require the response attribute to represent ordinal values that should increase with some of the explaining attributes. They are called classification problems with monotonicity constraints. Hyperrectangles can be viewed as storing objects in ℝn which can be used to learn concepts combining instance-based classification with the axis-parallel rectangle mainly used in rule induction systems. This hybrid paradigm is known as nested generalized exemplar learning. In this paper, we propose the selection of the most effective hyperrectangles by means of evolutionary algorithms to tackle monotonic classification. The model proposed is compared through an exhaustive experimental analysis involving a large number of data sets coming from real classification and regression problems. The results reported show that our evolutionary proposal outperforms other instance-based and rule learning models, such as OLM, OSDL, k-NN and MID; in accuracy and mean absolute error, requiring a fewer number of hyperrectangles.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 1
Pages
184 - 201
Publication Date
2016/01/20
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1146536How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Javier García
AU  - Adnan M. AlBar
AU  - Naif R. Aljohani
AU  - José-Ramón Cano
AU  - Salvador García
PY  - 2016
DA  - 2016/01/20
TI  - Hyperrectangles Selection for Monotonic Classification by Using Evolutionary Algorithms
JO  - International Journal of Computational Intelligence Systems
SP  - 184
EP  - 201
VL  - 9
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1146536
DO  - 10.1080/18756891.2016.1146536
ID  - García2016
ER  -