TY - JOUR
T1 - A Self-Adaptive Synthetic Over-Sampling Technique for Imbalanced Classification
AU - Gu, Xiaowei
AU - Angelov, Plamen Parvanov
AU - Soares, Eduardo Almeida
N1 - Publisher Copyright:
© 2020 Wiley Periodicals, Inc.
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest-for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.
AB - Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest-for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.
KW - fairness
KW - imbalanced classification
KW - performance boosting
KW - synthetic data generation
UR - http://www.research.lancs.ac.uk/portal/en/publications/a-selfadaptive-synthetic-oversampling-technique-for-imbalanced-classification(6703bb66-e51e-402c-aeb2-161cf33a02f4).html
UR - http://www.scopus.com/inward/record.url?scp=85083531984&partnerID=8YFLogxK
U2 - 10.1002/int.22230
DO - 10.1002/int.22230
M3 - Article
SN - 0884-8173
VL - 35
SP - 923
EP - 943
JO - International Journal of Intelligent Systems
JF - International Journal of Intelligent Systems
IS - 6
ER -