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Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks

Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks

Marco Vannucci, Valentina Colla, Silvia Cateni, Mirko Sgarbi
Copyright: © 2011 |Pages: 15
ISBN13: 9781609605513|ISBN10: 1609605519|EISBN13: 9781609605520
DOI: 10.4018/978-1-60960-551-3.ch021
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MLA

Vannucci, Marco, et al. "Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks." Computational Modeling and Simulation of Intellect: Current State and Future Perspectives, edited by Boris Igelnik, IGI Global, 2011, pp. 551-565. https://doi.org/10.4018/978-1-60960-551-3.ch021

APA

Vannucci, M., Colla, V., Cateni, S., & Sgarbi, M. (2011). Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks. In B. Igelnik (Ed.), Computational Modeling and Simulation of Intellect: Current State and Future Perspectives (pp. 551-565). IGI Global. https://doi.org/10.4018/978-1-60960-551-3.ch021

Chicago

Vannucci, Marco, et al. "Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks." In Computational Modeling and Simulation of Intellect: Current State and Future Perspectives, edited by Boris Igelnik, 551-565. Hershey, PA: IGI Global, 2011. https://doi.org/10.4018/978-1-60960-551-3.ch021

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Abstract

In this chapter a survey on the problem of classification tasks in unbalanced datasets is presented. The effect of the imbalance of the distribution of target classes in databases is analyzed with respect to the performance of standard classifiers such as decision trees and support vector machines, and the main approaches to improve the generally not satisfactory results obtained by such methods are described. Finally, two typical applications coming from real world frameworks are introduced, and the uses of the techniques employed for the related classification tasks are shown in practice.

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