A self‐adaptive synthetic over‐sampling technique for imbalanced classification

Abstract

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 socalled 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 Xiaowei Gu and Plamen P. Angelov contributed equally to this study.

Radiomics and Artificial Intelligence Analysis with Textural Metrics Extracted by Contrast-Enhanced Mammography in the Breast Lesions Classification

“…In addition, the presence of less-represented classes has led the use of two techniques to synthesize data and, therefore, to help balance the classes (malignant and benign) giving the possibility to better train the considered classifiers: the self-adaptive synthetic over-sampling (SASYNO) approach and the adaptive synthetic sampling (ADASYN) approach. This allowed to boost the performance both overall and in terms of confusion matrix [ 47 , 48 , 49 , 50 , 51 ].…”

Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference

“…Then, it imposes a Gaussian disturbance on these data samples, and, finally, it generates synthetic samples by creating linear interpolations between these extrapolations. A further difference from our recent method [13] is that in this paper we use the standard deviation, σ as a radius of influence around the prototype rather than absolute distance of first order. We then augment the training data set with this synthetically generated data set as shown in Fig.…”

“…We achieve this by synthetic data augmentation. In this paper we propose a different approach from our recently published one [13] for synthesising data for highly imbalanced classification problems. The differences are that in this paper we synthesise data around prototypes which makes these synthetic data more likely to have the same class as the prototype.…”

Learning to learn by yourself: Unsupervised meta‐learning with self‐knowledge distillation for COVID‐19 diagnosis from pneumonia cases

“…Few‐shot learning : Few‐shot learning, based on meta‐learning, typically uses episodic training strategies 31,32 . In each episode, the model based on meta‐learning is trained on a meta‐task, which can be viewed as a classification task 33,34 . During training, the tasks were randomly selected from the training data set in the episodes.…”