By Topic

Enhancing classification performance of multi-class imbalanced data using the OAA-DB algorithm

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Jeatrakul, P. ; Sch. of Inf. Technol., Mae Fah Luang Univ., Chiang Rai, Thailand ; Kok Wai Wong

In data classification, the problem of imbalanced class distribution has attracted many attentions. Most efforts have used to investigate the problem mainly for binary classification. However, research solutions for the imbalanced data on binary-class problems are not directly applicable to multi-class applications. Therefore, it is a challenge to handle the multi-class problem with imbalanced data in order to obtain satisfactory results. This problem can indirectly affect how human visualise the data. In this paper, an algorithm named One-Against-All with Data Balancing (OAA-DB) is developed to enhance the classification performance in the case of the multi-class imbalanced data. This algorithm is developed by combining the multi-binary classification technique called One-Against-All (OAA) and a data balancing technique. In the experiment, the three multi-class imbalanced data sets used were obtained from the University of California Irvine (UCI) machine learning repository. The results show that the OAA-DB algorithm can enhance the classification performance for the multi-class imbalanced data without reducing the overall classification accuracy.

Published in:

Neural Networks (IJCNN), The 2012 International Joint Conference on

Date of Conference:

10-15 June 2012