Journal article
An Ensemble Imbalanced Data Classification Algorithm Based on Random k-Rank Nearest Neighbor Rules
Advances in Applied Mathematics, Vol.9(5), pp.622-629
2020
Abstract
Aiming at the problem of imbalanced data classification, in order to improve the low classification accuracy of minority class samples in binary classification tasks, this paper proposes a random rank k-nearest neighbor ensemble learning algorithm, REKRNN. This method applies the rank k-nearest neighbor algorithm to the Bagging ensemble learning framework, and uses hybrid resampling and random subspace method to balance the training set and increase the diversity of base learners. Simulation experiments show that the algorithm performs well in handling imbalanced data classification tasks.
In this article, a random ensemble k-RNN algorithm called REKRNN is proposed to deal with the imbalanced data classification. The algorithm incorporates the k-rank nearest neighbor classifier into the frame of Bagging algorithm. At the same time, resampling techniques and random feature method are applied to deal with the imbalanced issue. We observe that the proposed method performed remarkably well on different imbalanced dataset.
Details
- Title
- An Ensemble Imbalanced Data Classification Algorithm Based on Random k-Rank Nearest Neighbor Rules
- Publication Details
- Advances in Applied Mathematics, Vol.9(5), pp.622-629
- Resource Type
- Journal article
- Publisher
- Hans Publishers, Inc.,Hansi Chubanshe
- Copyright
- Permission granted to the University of West Florida Libraries by the author to digitize and/or display this information for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires the permission of the copyright holder.
- Identifiers
- 99381378976606600
- Academic Unit
- Mathematics and Statistics; Hal Marcus College of Science and Engineering
- Language
- Chinese; English