By Topic

Fuzzy-rough k-nearest neighbor algorithm for imbalanced data sets learning

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)
Hui Han ; Sch. of Inf. Sci. & Technol., Beijing Forestry Univ., Beijing, China ; Binghuan Mao

Learning from imbalanced data sets presents a new challenge to machine learning community, as traditional methods are biased to majority classes and produce poor detection rate of minority classes. This paper presents a new approach, namely fuzzy-rough k-nearest neighbor algorithm for imbalanced data sets learning to improve the classification performance of minority class. The approach defines fuzzy membership function that is in favor of minority class and constructs fuzzy equivalent relation between the unlabeled instance and its k nearest neighbors. The approach takes the fuzziness and roughness of the nearest neighbors of an instance into consideration, and can reduce the disturbance of majority class to minority class. Experiments show that our new approach improves not only the classification performance of minority class more effectively, but also the classification performance of the whole data set comparing with other methods.

Published in:

Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on  (Volume:3 )

Date of Conference:

10-12 Aug. 2010