A novel dynamic ensemble selection classifier for an imbalanced data set: An application for credit risk assessment

Hou, Wenhui; Wang, Xiaokang; Zhang, Hongyu; Wang, Jianqiang; Lin, Li

doi:10.1016/j.knosys.2020.106462

Cited by 67 publications

(29 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wang et al [6] proposed an entropy and confidence-based under-sampling boosting framework to solve imbalanced problems. Hou et al [7] initially used the synthetic minority over-sampling technique to balance a training set before generating a candidate classifier pool. Chawla et al [8] proposed the Synthetic Minority Over-sampling Technique (SMOTE).…”

Section: Sampling Techniquesmentioning

confidence: 99%

An Intelligent Fusion Algorithm and Its Application Based on Subgroup Migration and Adaptive Boosting

Yang

et al. 2021

Symmetry

View full text Add to dashboard Cite

Imbalanced data and feature redundancies are common problems in many fields, especially in software defect prediction, data mining, machine learning, and industrial big data application. To resolve these problems, we propose an intelligent fusion algorithm, SMPSO-HS-AdaBoost, which combines particle swarm optimization based on subgroup migration and adaptive boosting based on hybrid-sampling. In this paper, we apply the proposed intelligent fusion algorithm to software defect prediction to improve the prediction efficiency and accuracy by solving the issues caused by imbalanced data and feature redundancies. The results show that the proposed algorithm resolves the coexisting problems of imbalanced data and feature redundancies, and ensures the efficiency and accuracy of software defect prediction.

show abstract

Section: Sampling Techniquesmentioning

confidence: 99%

An Intelligent Fusion Algorithm and Its Application Based on Subgroup Migration and Adaptive Boosting

Yang

et al. 2021

Symmetry

View full text Add to dashboard Cite

show abstract

“…Ensemble learning can improve the generalization ability to existing algorithms and has been proven to be an effective method for imbalanced data processing. Among the many ensemble learning approaches, dynamic ensemble selection (DES) as a very promising method has been proved by a large number of studies to be superior to static ensemble learning [19] , [20] , [21] . For a DES technique, the neighbors of each test sample, called the competence region, are used to measure the competence of each candidate classifier.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, more and more DES methods have been applied to deal with imbalanced data problem and have achieved outstanding performance [ 20 , 22 , 23 ]. Roy, Cruz [24] compared the performance of DES combined with a preprocessing technique and static ensemble for processing imbalanced data.…”

Section: Introductionmentioning

confidence: 99%

“…The experimental results show that DES have higher F-measure and G-mean relative to static ensemble methods. Hou, Wang [20] combined SMOTE and DES to assess credit risk, and tested the performance of the hybrid method on other 15 imbalanced datasets. However, these DES methods mainly use bagging [25] to train candidate classifiers, and the training sets randomly generated by bagging may not be sufficient to represent the competence regions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A novel combined dynamic ensemble selection model for imbalanced data to detect COVID-19 from complete blood count

Shen

et al. 2021

Computer Methods and Programs in Biomedicine

View full text Add to dashboard Cite

Background : As blood testing is radiation-free, low-cost and simple to operate, some researchers use machine learning to detect COVID-19 from blood test data. However, few studies take into consideration the imbalanced data distribution, which can impair the performance of a classifier. Method : A novel combined dynamic ensemble selection (DES) method is proposed for imbalanced data to detect COVID-19 from complete blood count. This method combines data preprocessing and improved DES. Firstly, we use the hybrid synthetic minority over-sampling technique and edited nearest neighbor (SMOTE-ENN) to balance data and remove noise. Secondly, in order to improve the performance of DES, a novel hybrid multiple clustering and bagging classifier generation (HMCBCG) method is proposed to reinforce the diversity and local regional competence of candidate classifiers. Results : The experimental results based on three popular DES methods show that the performance of HMCBCG is better than only use bagging. HMCBCG+KNE obtains the best performance for COVID-19 screening with 99.81% accuracy, 99.86% F1, 99.78% G-mean and 99.81% AUC. Conclusion : Compared to other advanced methods, our combined DES model can improve accuracy, G-mean, F1 and AUC of COVID-19 screening.

show abstract

“…The models developed to tackle this task use data from previous completed loans and try to determine if a borrower has fully repaid the loan or has defaulted (BAESENS; ROESCH; SCHEULE, 2016). Recent efforts have been made to improve credit scoring models (ZHANG, W. et al, 2021;XIAO et al, 2020;ZHANG, T. et al, 2018;LESSMANN et al, 2015), specially in the Peerto-peer (P2P) lending context, due to the public availability of data from P2P lending platforms such as Prosper and Lending Club (MOSCATO;PICARIELLO;SPERLÍ, 2021;MANCISIDOR et al, 2020;HOU et al, 2020;LI et al, 2017). Despite of technological advancements, the most commonly used models by credit bureaus like Experian are linear (EXPERIAN, 2021).…”

Section: List Of Figuresmentioning

confidence: 99%

Markov Blanket discovery without causal sufficiency: application in credit data

Jeronymo¹

View full text Add to dashboard Cite

Faster feature selection algorithms become a necessity as Big Data dictates the zeitgeist. An important class of feature selectors are Markov Blanket (MB) learning algorithms. They are Causal Discovery algorithms that learn the local causal structure of a target variable. A common assumption in their theoretical basis, yet often violated in practice, is causal sufficiency. The M3B algorithm was proposed as the first to directly learn the MB without demanding causal sufficiency. The main drawback of M3B is that it is time inefficient, being intractable for high-dimensional inputs. Intending a faster method, we derive the Fast Markov Blanket Discovery Algorithm (FMMB). Empirical results that compare FMMB to M3B on the structural learning task show that FMMB outperforms M3B in terms of time efficiency, while preserving structural accuracy given a large enough sample size. Moreover, we introduce a new technique to aggregate bootstrapped MB structures, that first extracts a consensus MB, than constructs the aggregated structure as the union of the most probable path between each feature in the MB and the target. Comparisons with the state of the art shows that the proposed aggregation has a smaller loss of information. The analysis was conducted by using Credit-related data, with special focus on Peer-to-Peer lending platforms. Our results validate the credit scoring models used by these platforms as effective in identifying bad borrowers, yet still have room for improvement. Finally, we propose an ensemble of Bayesian Network Classifiers trained using the Cross-Entropy method. The ensemble performs better in credit scoring than Logistic Regression and Random Forests in the selected datasets.

show abstract

A novel dynamic ensemble selection classifier for an imbalanced data set: An application for credit risk assessment

Cited by 67 publications

References 48 publications

An Intelligent Fusion Algorithm and Its Application Based on Subgroup Migration and Adaptive Boosting

An Intelligent Fusion Algorithm and Its Application Based on Subgroup Migration and Adaptive Boosting

A novel combined dynamic ensemble selection model for imbalanced data to detect COVID-19 from complete blood count

Markov Blanket discovery without causal sufficiency: application in credit data

Contact Info

Product

Resources

About