Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks

Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks

Marco Vannucci (Scuola Superiore Sant’Anna, Italy), Valentina Colla (Scuola Superiore Sant’Anna, Italy), Silvia Cateni (Scuola Superiore Sant’Anna, Italy) and Mirko Sgarbi (Scuola Superiore Sant’Anna, Italy)
DOI: 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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Classification Tasks With Unbalanced Datasets

The detrimental effect of dataset imbalance on the predictive performances of standard classifiers can be observed in most of the datasets affected by such drawback. The performance reduction involves both the rate of overall correct classifications and the rate of rare events detected that – as it was previously mentioned – is a key issue for most applications and contributes to making the problem of the classification tasks with unbalanced datasets very critical.

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