Progressive Hybrid Classifier Ensemble for Imbalanced Data

Yang, Kaixiang; Yu, Zhiwen; Chen, C. L. Philip; Cao, Wenming; Wong, Hau-San; You, Jane; Han, Guoqiang

doi:10.1109/tsmc.2021.3051138

Cited by 22 publications

(5 citation statements)

References 56 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adaptive two-stage under sampling (ATUP) and metric-based data space transformation (MDST) are the core components of the methodology. For a well-rounded dataset, they employ MDST to locate the proper embedding space and ATUP to select representative samples [29]. Traditional oversampling approaches create fresh samples locally, leading to poor generalisation capacity and unable to deliver improved classification judgements, and are thus among the methods based on the data level that have the potential to improve accuracy.…”

Section: A Data-level Approachmentioning

confidence: 99%

A New GAN-based data augmentation method for Handling Class Imbalance in Credit Card Fraud detection

Strelcenia

Prakoonwit

2023

2023 10th International Conference on Signal Processing and Integrated Networks (SPIN)

View full text Add to dashboard Cite

One of the most common cybercrimes that people encounter is credit card fraud. Systems for identifying fraudulent transactions that are based on intelligent machine learning are particularly successful in real-world situations. Nevertheless, when creating these systems, machine learning algorithms face the issue of imbalanced data or an unbalanced distribution of classes. Because of this, balancing the dataset becomes a crucial sub-task. A review of cutting-edge methods highlights the necessity for a thorough assessment of class imbalance management techniques in order to create a smart and effective system to identify fraudulent transactions. The goal of the current study is to compare several strategies for dealing with class imbalance. Therefore, the present study compares the performance of our novel K-CGAN method with SMOTE, B-SMOTE, and ADASYN in terms of Recall, F1-score, Accuracy, and Precision. The result shows that novel K-CGANs generated high quality test dataset and performs better as compared to other resampling techniques.

show abstract

Section: A Data-level Approachmentioning

confidence: 99%

A New GAN-based data augmentation method for Handling Class Imbalance in Credit Card Fraud detection

Strelcenia

Prakoonwit

2023

2023 10th International Conference on Signal Processing and Integrated Networks (SPIN)

View full text Add to dashboard Cite

show abstract

“…BalanceCascade (Liu et al, 2009 ) splits the majority class samples as several subsets and trains AdaBoost classifiers based on the subsets. Yang et al ( 2021 ) proposes an ensemble framework based on subspace feature space ensemble and metric learning.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Fusion Based Method for Imbalanced Data Classification

et al. 2022

Self Cite

View full text Add to dashboard Cite

The imbalance problem is widespread in real-world applications. When training a classifier on the imbalance datasets, the classifier is hard to learn an appropriate decision boundary, which causes unsatisfying classification performance. To deal with the imbalance problem, various ensemble algorithms are proposed. However, conventional ensemble algorithms do not consider exploring an effective feature space to further improve the performance. In addition, they treat the base classifiers equally and ignore the different contributions of each base classifier to the ensemble result. In order to address these problems, we propose a novel ensemble algorithm that combines effective data transformation and an adaptive weighted voting scheme. First, we utilize modified metric learning to obtain an effective feature space based on imbalanced data. Next, the base classifiers are assigned different weights adaptively. The experiments on multiple imbalanced datasets, including images and biomedical datasets verify the superiority of our proposed ensemble algorithm.

show abstract

“…The diversity of the base classifiers of this method mainly comes from resampling techniques, but it still needs to use prior knowledge to select the two‐tuples of the model. Yang et al 62 proposed a hybrid classifier ensemble framework using metric data space transformation and an adaptive two‐stage under‐sampling method. In this method, the goal of MDST is to find a more suitable embedding space for the original imbalanced data set.…”

Section: Relate Workmentioning

confidence: 99%

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Dai¹,

Liu²,

Yang

2022

Computational Intelligence

View full text Add to dashboard Cite

One of the most widely used approaches to the class‐imbalanced issue is ensemble learning. The base classifier is trained using an unbalanced training set in the conventional ensemble learning approach. We are unable to select the best suitable resampling method or base classifier for the training set, despite the fact that researchers have examined employing resampling strategies to balance the training set. A multi‐armed bandit heterogeneous ensemble framework was developed as a solution to these issues. This framework employs the multi‐armed bandit technique to pick the best base classifier and resampling techniques to build a heterogeneous ensemble model. To obtain training sets, we first employ the bagging technique. Then, we use the instances from the out‐of‐bag set as the validation set. In general, we consider the basic classifier combination with the highest validation set score to be the best model on the bagging subset and add it to the pool of model. The classification performance of the multi‐armed bandit heterogeneous ensemble model is then assessed using 30 real‐world imbalanced data sets that were gathered from UCI, KEEL, and HDDT. The experimental results demonstrate that, under the two assessment metrics of AUC and Kappa, the proposed heterogeneous ensemble model performs competitively with other nine state‐of‐the‐art ensemble learning methods. At the same time, the findings of the experiment are confirmed by the statistical findings of the Friedman test and Holm's post‐hoc test.

show abstract

Progressive Hybrid Classifier Ensemble for Imbalanced Data

Cited by 22 publications

References 56 publications

A New GAN-based data augmentation method for Handling Class Imbalance in Credit Card Fraud detection

A New GAN-based data augmentation method for Handling Class Imbalance in Credit Card Fraud detection

Adaptive Fusion Based Method for Imbalanced Data Classification

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Contact Info

Product

Resources

About