Special Topics: Boosting for Learning Multiple Classes with Imbalanced Class Distribution

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The special topic calls for papers on Boosting for Learning Multiple Classes with Imbalanced Class Distribution and such papers will appear in Studies in Sociology of Science as a special column.

Affiliated research area: Classification of DataSocial Structure, Classes


Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. This learning difficulty attracts a lot of research interests. Most efforts concentrate on bi-class problems. However, bi-class is not the only scenario where the class imbalance problem prevails. Reported solutions for bi-class applications are not applicable to multi-class problems. In this paper, we develop a cost-sensitive boosting algorithm to improve the classification performance of imbalanced data involving multiple classes. One barrier of applying the cost-sensitive boosting algorithm to the imbalanced data is that the cost matrix is often unavailable for a problem domain. To solve this problem, we apply Genetic Algorithm to search the optimum cost setup of each class. Empirical tests show that the proposed cost-sensitive boosting algorithm improves the classification performances of imbalanced data sets significantly.


In addition to the Review and Original Articles by invited speakers, we are inviting you to submit a relevant research paper on Boosting for Learning Multiple Classes with Imbalanced Class Distribution for consideration. Papers will be subject to normal peer review and must comply with the Guide for Authors.

To submit papers to the “Boosting for Learning Multiple Classes with Imbalanced Class Distribution” Special Topic, please go to http://www.cscanada.net. With your submission, please state clearly to the editor that your manuscripts are submitted to the Special Topic Boosting for Learning Multiple Classes with Imbalanced Class Distribution.


Related Conferences:

Sixth IEEE International Conference on Data Mining (ICDM'06)

Related Journals (Special issue):

Studies in Sociology of Science, ISSN 1923-0176 [Print], ISSN 1923-0184 [Online]. http://cscanada.net/index.php/sss/index

Related Articles:

Boosting for Learning Multiple Classes with Imbalanced Class Distribution ISBN: 0-7695-2701-9


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