Galvan, Edgar and Vazquez-Mendoza, Lucia and Trujillo, Leonardo (2016) Stochastic Semantic-Based Multi-objective Genetic Programming Optimisation for Classification of Imbalanced Data. Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings, Part 11. pp. 261-273. ISSN 0302-9743

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Abstract

Data sets with imbalanced class distribution pose serious challenges to well-established classifiers. In this work, we propose a stochastic multi-objective genetic programming based on semantics. We tested this approach on imbalanced binary classification data sets, where the proposed approach is able to achieve, in some cases, higher recall, precision and F-measure values on the minority class compared to C4.5, Naive Bayes and Support Vector Machine, without significantly decreasing these values on the majority class.

Item Type:	Article
Keywords:	Stochastic; Semantic-Based; Multi-objective; Genetic Programming Optimisation; Classification; Imbalanced Data;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Research Institutes > Hamilton Institute
Item ID:	15348
Identification Number:	https://doi.org/10.1007/978-3-319-62428-0
Depositing User:	Edgar Galvan
Date Deposited:	26 Jan 2022 13:34
Journal or Publication Title:	Advances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings, Part 11
Publisher:	Springer
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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