GM4OS: An Evolutionary Oversampling Approach for Imbalanced Binary Classification Tasks

Created by W.Langdon from gp-bibliography.bib Revision:1.7650

@InProceedings{Farinati:2024:evoapplications,

• author = "Davide Farinati and Leonardo Vanneschi",
• title = "GM4OS: An Evolutionary Oversampling Approach for Imbalanced Binary Classification Tasks",
• booktitle = "27th International Conference, EvoApplications 2024",
• year = "2024",
• editor = "Stephen Smith and Joao Correia and Christian Cintrano",
• series = "LNCS",
• volume = "14634",
• publisher = "Springer",
• address = "Aberystwyth",
• month = "3-5 " # apr,
• pages = "68--82",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Oversampling, Imbalanced Data, Binary Classification",
• isbn13 = "978-3-031-56851-0",
• URL = "https://rdcu.be/dDZNg",
• DOI = "doi:10.1007/978-3-031-56852-7_5",
• size = "15 pages",
• abstract = "Imbalanced datasets pose a significant and longstanding challenge to machine learning algorithms, particularly in binary classification tasks. Over the past few years, various solutions have emerged, with a substantial focus on the automated generation of synthetic observations for the minority class, a technique known as oversampling. Among the various oversampling approaches, the Synthetic Minority Oversampling Technique (SMOTE) has recently garnered considerable attention as a highly promising method. SMOTE achieves this by generating new observations through the creation of points along the line segment connecting two existing minority class observations. Nevertheless, the performance of SMOTE frequently hinges upon the specific selection of these observation pairs for resampling. This research introduces the Genetic Methods for Over Sampling (GM4OS), a novel oversampling technique that addresses this challenge. In GM4OS, individuals are represented as pairs of objects. The first object assumes the form of a GP-like function, operating on vectors, while the second object adopts a GA-like genome structure containing pairs of minority class observations. By co-evolving these two elements, GM4OS conducts a simultaneous search for the most suitable resampling pair and the most effective oversampling function. Experimental results, obtained on ten imbalanced binary classification problems, demonstrate that GM4OS consistently outperforms or yields results that are at least comparable to those achieved through linear regression and linear regression when combined with SMOTE.",
• notes = "http://www.evostar.org/2024/ EvoApplications2024 held in conjunction with EuroGP'2024, EvoCOP2024 and EvoMusArt2024",

}

Genetic Programming entries for Davide Farinati Leonardo Vanneschi

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