Combined weighted multi-objective optimizer for instance reduction in two-class imbalanced data problem

TL;DR: The weighted-feature strategy and the cost-sensitive learning are integrated effectively in a multi-kernel support vector machine model, to solve the problems brought by between-class imbalance and heterogeneous signals in fault detection.

TL;DR: Li et al. as mentioned in this paper proposed a transfer learning classifier (TLC) based on transfer learning to deal with class-imbalanced data, which includes active sampling module, real-time data augmentation module and DenseNet module.

TL;DR: In this paper , an efficient evolutionary strategy with non-dominated sorting, environmental selection, and an archiving mechanism is designed to optimize the false positive rate, the false negative rate and reduce the size of the resulting tree.

TL;DR: In this paper , an end-to-end deep learning semantic segmentation network Fault-Seg-Net is proposed to identify fault on seismic images, which simultaneously learns global semantic features and local detailed features.

TL;DR: This handbook provides you with everything you need to know about parametric and nonparametric statistical procedures, and helps you choose the best test for your data, interpret the results, and better evaluate the research of others.

TL;DR: Two new minority over-sampling methods are presented, borderline- SMOTE1 and borderline-SMOTE2, in which only the minority examples near the borderline are over- Sampling, which achieve better TP rate and F-value than SMOTE and random over-Sampling methods.

TL;DR: A taxonomy for ensemble-based methods to address the class imbalance where each proposal can be categorized depending on the inner ensemble methodology in which it is based is proposed and a thorough empirical comparison is developed by the consideration of the most significant published approaches to show whether any of them makes a difference.

TL;DR: This paper presents a novel approach for learning from imbalanced data sets, based on a combination of the SMOTE algorithm and the boosting procedure, which shows improvement in prediction performance on the minority class and overall improved F-values.