Irrelevant attribute resistance approach to binary classification for imbalanced data

Abstract

Imbalanced data distribution is a common feature in real-world datasets. For imbalanced data, the imbalanced characteristics of the classes have two negative effects on classification results, one of which is that the minority class is highly likely masked by the majority class so as to weaken the ability of the classifiers to identify the minority class. Another effect is that irrelevant attributes hidden in imbalanced data can create much noise to interfere the classifiers, thereby leading to that the classifiers could mistakenly treat noise as the minority classes. In this scenario, the performance of the classifiers is rapidly declined and the classifiers obtain incorrect classification results. To address this issue, this paper proposed a conformal transformation twin-hypersphere with fuzzy. The critical thought is that using conformal transformation to explore the regions containing minority classes, by so doing, minority classes can be more likely to be noticed by the classifier. Using the proposed fuzzy function assesses the contributions of points to the hypersphere training, through evaluating the contributions, noise can be determined, thereby increasing the ability of the classifier to noise resistance. Results on the synthetic and real datasets show that the proposed method outperforms the competitors in classification accuracy and noise resistance. Results also imply that the proposed method does not exhibit exponential calculation time, meaning that the method is suitable for the classification of large-scale imbalanced datasets. We demonstrate that conformal transformation can assist those non-linear kernels to find those hard-to-observe regions containing minority classes, thereby strengthening the adaptability of the classifiers to imbalanced data following complex distributions. Moreover, the non-linear kernels using conformal transformation can adapt to the situation where different sub-regions in sample space require different nonlinearities.