Mathematical software

SMOTE
Referenced in 152 articles [sw34239]
construction of classifiers from imbalanced datasets is described. A dataset is imbalanced if the classification...

Imbalanced-learn
Referenced in 10 articles [sw21535]
toolbox to tackle the curse of imbalanced datasets in machine learning. imbalanced-learn ... cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition...

iSuc-PseOpt
Referenced in 23 articles [sw22429]
effects into pseudo components and optimizing imbalanced training dataset. Succinylation is a posttranslational modification...

iPPBS-Opt
Referenced in 16 articles [sw22428]
Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets. Knowledge of protein-protein interactions...

ImbTreeEntropy
Referenced in 2 articles [sw40352]
building entropy-based classification trees on imbalanced datasets. In this paper, we propose a novel ... gain. Additionally, ImbTreeEntropy is able to handle imbalanced data, which is a challenging issue...

ImbTreeAUC
Referenced in 2 articles [sw40353]
under the ROC curve (AUC) on imbalanced datasets. In this paper, we propose a novel ... global AUC measures. Additionally, ImbTreeAUC can handle imbalanced data, which is a challenging issue...

imbalance
Referenced in 1 article [sw32594]
package imbalance: Preprocessing Algorithms for Imbalanced Datasets. Class imbalance usually damages the performance of classifiers...

LCE
Referenced in 5 articles [sw25277]
results on real gene expression and synthetic datasets indicate that LCE: (i) usually outperforms ... especially with the presence of noise and imbalanced data clusters; (iii) provides a high-level ... computationally efficient for large datasets and gene clustering. AVAILABILITY: Online supplementary and implementation are available...

extended_rmcv.m
Referenced in 2 articles [sw34281]
matrices, twenty-three artificial datasets, seventeen UCI datasets, and different performance measures, we show that ... error severity is important) and/or imbalanced problems (which are most real-life classification problems ... information should be maximized, the data is imbalanced, and/or the problem is ordinal, whether ... code, datasets, and results are publicly available url{http://www.ee.bgu.ac.il/ boaz/software...

ACOSampling
Referenced in 5 articles [sw41786]
colony optimization-based undersampling method for classifying imbalanced DNA microarray data. In DNA microarray data ... method on four benchmark skewed DNA microarray datasets by support vector machine (SVM) classifier, showing...

AUC4.5
Referenced in 1 article [sw40358]
modification of Quinlan’s C4.5 algorithm for imbalanced data classification. While the C4.5 algorithm uses ... experimental study was performed on 20 real datasets from the machine learning repository...

ivadomed
Referenced in 1 article [sw35828]
extra information during training and evaluation. Any dataset following the Brain Imaging Data Structure (BIDS ... aleatoric and epistemic), and losses adapted to imbalanced classes and non-binary predictions. Each step...

RB-CCR
Referenced in 1 article [sw41067]
standard approach to improving classification performance on imbalanced binary data. However, the state ... cross-validated results on 57 benchmark binary datasets with 9 classifiers show that...

SMOTE