Data Mining for Imbalanced Datasets: An Overview View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2010-07-07

AUTHORS

Nitesh V. Chawla

ABSTRACT

A dataset is imbalanced if the classification categories are not approximately equally represented. Recent years brought increased interest in applying machine learning techniques to difficult “real-world” problems, many of which are characterized by imbalanced data. Additionally the distribution of the testing data may differ from that of the training data, and the true misclassification costs may be unknown at learning time. Predictive accuracy, a popular choice for evaluating performance of a classifier, might not be appropriate when the data is imbalanced and/or the costs of different errors vary markedly. In this Chapter, we discuss some of the sampling techniques used for balancing the datasets, and the performance measures more appropriate for mining imbalanced datasets. More... »

PAGES

875-886

Book

TITLE

Data Mining and Knowledge Discovery Handbook

ISBN

978-0-387-09822-7
978-0-387-09823-4

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-0-387-09823-4_45

DOI

http://dx.doi.org/10.1007/978-0-387-09823-4_45

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1001721707


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