Sample and feature selecting based ensemble learning for imbalanced problems

Wang, Zhe; Peng, Jilun; Xu, Xinlei; Wang, Bolu; Zhu, Yong; Li, Dongdong

doi:10.1016/j.asoc.2021.107884

Cited by 18 publications

(7 citation statements)

References 40 publications

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“…In our experiment, the number of model pools ( T ) needs to be set by us. In Reference 75, experiments show that a stable performance can be obtained when the number of bagging series algorithm trees is 30. Therefore, in our experiment, we set the number of model pools ( T = 30).…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Dai¹,

Liu²,

Yang

2022

Computational Intelligence

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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.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Dai¹,

Liu²,

Yang

2022

Computational Intelligence

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“…As single learning algorithms are usually limited and make errors, ensemble learning can help improve detections by taking advantage of the strengths of different learning algorithms [102]. Further, ensemble learning can easily be coupled with sampling methods, improve detection with a new aggregating strategy, or integrate itself with another ensemble learning for hybrid learning strategies [103].…”

Section: Discussionmentioning

confidence: 99%

Detecting Frauds and Payment Defaults on Credit Card Data Inherited With Imbalanced Class Distribution and Overlapping Class Problems: A Systematic Review

Kalid,

Khor,

et al. 2024

IEEE Access

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Credit card payments are one popular e-payment option apart from cash payments. Recent reports show that credit card fraud and payment defaults are increasing annually and are alarming. Thus, researchers have attempted various machine learning techniques to address these two challenges. However, they are challenged to mitigate the two major problems inherited in credit card data: (i) imbalanced class distribution and (ii) overlapping classes. Mitigating these problems shall effectively detect credit card frauds and payment defaults, thus benefiting card issuers and holders. Hence, this paper aims to develop a systematic review using PRISMA to identify and compare various credit card datasets, machine learning techniques, and evaluation metrics. Subsequently, we provide recommendations for handling these two problems. We extracted research papers from 2016 to 2023 from ScienceDirect, Springer, Association and Computing Machinery (ACM), and IEEE databases. The papers shall be included if written in English and published in peer-reviewed and indexed journals or conference proceedings. Finally, 87 papers were selected based on the eligibility criteria. Based on our findings, the European and Taiwan datasets are widely used in the research community. However, most researchers focus on tackling imbalanced class distribution rather than two problems together. We recommended to the research community the application of deep learning, ensemble learning, and sampling methods to effectively detect fraud and payment defaults on credit card datasets that inherit the two problems. In evaluating the machine learning algorithms, we recommend using metrics that can separately evaluate the algorithms' performance in detecting frauds/payment defaults and normal transactions.INDEX TERMS PRISMA, credit card fraud, payment default, imbalanced class distribution, overlapping classes.

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“…Literature suggests, choosing the proper evaluation metric, proper selection of re‐sampling strategies, proper use of SMOTE and its variants and also the design of balanced bagging classifiers are proving their effectiveness in this problem domain (Daud et al, 2023; Elreedy & Atiya, 2019; Kim et al, 2019; Wei et al, 2022). It is also studied that, the use of

k

‐fold cross validation, ensemble strategies, re‐sampling with different ratio; clustering the abundant class in

n

number of groups (

n

being the number of cases in

n

) using medoid as center of the cluster and then training the classifier with the rare class and the mediods only; designing the models to balance the classes using machine learning (ML), soft computing (SC) and deep learning (DL) techniques (Gao et al, 2020; Hongle et al, 2021; Kaisar & Chowdhury, 2022; Saini & Susan, 2022; Sidumo et al, 2022; Wang et al, 2021) are being widely used in this area of research. The use of SC and ML based strategies for generating synthetic samples for improving the classification accuracy has motivated us to develop a hybridized approach of generating synthetic samples.…”

Section: Overviewmentioning

confidence: 99%

An empirical hybridized Siamese network using hypercube natural aggregation algorithm for handling imbalance data learning

Rout

Mallick

Reddy³

et al. 2023

Expert Systems

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Dealing with imbalanced data is a common challenge in machine learning, where one class has significantly fewer examples than another. Successfully addressing this challenge requires careful consideration of the data, algorithm, and evaluation metrics to ensure that the model accurately predicts the minority class. In this study, we present a hybrid approach called Siamese‐HYNAA, which combines a Siamese network and a population‐based optimizer hypercube natural aggregation algorithm (HYNAA) to generate candidate solutions for augmenting the minority class. We collected 10 imbalanced datasets ranging from 1.81 to 8.78 imbalanced ratios and built solution pairs based on correctly predicted candidate solutions using support vector machine (SVM). We then fed these solutions to the Siamese network, which employs a one‐shot learning approach to improve predictions with fewer candidate solutions. However, we found that SVM predicted only a small number of minority class samples accurately, prompting us to optimize the number of candidate solution pairs using HYNAA to generate more synthetic samples for the Siamese network. We evaluated our proposed strategy against basic SMOTE and our previous work, SMOTE‐PSOEV, using various performance measures, including ROC‐AUC learning curves, sensitivity, specificity, accuracy, Characteristic stability index, balanced accuracy, F1‐score, informedness, markedness, and execution time. Our results indicate that Siamese‐HYNAA generates promising results for imbalanced data.

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Sample and feature selecting based ensemble learning for imbalanced problems

Cited by 18 publications

References 40 publications

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Multi‐armed bandit heterogeneous ensemble learning for imbalanced data

Detecting Frauds and Payment Defaults on Credit Card Data Inherited With Imbalanced Class Distribution and Overlapping Class Problems: A Systematic Review

An empirical hybridized Siamese network using hypercube natural aggregation algorithm for handling imbalance data learning

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