Abstract: In this paper a new algorithm, OKC classifier is proposed that is a hybrid of One-Class SVM, k-Nearest Neighbours and CART algorithms. The performance of most of the classification algorithms is significantly influenced by certain characteristics of datasets on which these are modeled such as imbalance in class distribution, class overlapping, lack of density, etc. The proposed algorithm can perform the classification task on imbalanced datasets without re-sampling. This algorithm is compared against a few well known classification algorithms and on datasets having varying degrees of class imbalance and class overlap. The experimental results demonstrate that the proposed algorithm has performed better than a number of standard classification algorithms.

Maruthi Rohit Ayyagari, “Classification of Imbalanced Datasets using One-Class SVM, k-Nearest Neighbors and CART Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 11(11), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111101

@article{Ayyagari2020,
title = {Classification of Imbalanced Datasets using One-Class SVM, k-Nearest Neighbors and CART Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111101},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111101},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {11},
author = {Maruthi Rohit Ayyagari}
}