A New Method For Clustering In Credit Scoring Problems

Gholamian, Mohammad Reza; Jahanpour, Saber; Sadatrasoul, Seyed Mahdi

doi:10.22436/jmcs.06.02.02

Cited by 8 publications

(7 citation statements)

References 24 publications

(30 reference statements)

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“…Clustering is designed to explore the hidden patterns or features in the data, which is widely used in feature learning, summarizing and explaining key features of the unlabelled data (Gholamian et al, 2013). Semisupervised clustering can find homogeneous clusters among samples of similar features.…”

Section: Methodsmentioning

confidence: 99%

“…(), but clustering is rarely employed in credit scoring (Chen et al ., ). Clustering is usually only applied in the variable‐selection process to improve traditional algorithms (Gholamian et al ., ; Hand and Henley, ; Hsieh, ).…”

Section: Introductionmentioning

confidence: 99%

“…The basic instance-based algorithm K nearest neighbours (KNNs) has been adopted to assess credit risk in some studies such as Alam et al (2000) and Guo et al (2016), but clustering is rarely employed in credit scoring . Clustering is usually only applied in the variable-selection process to improve traditional algorithms (Gholamian et al, 2013;Hand and Henley, 1997;Hsieh, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Inferring the Outcomes of Rejected Loans: An Application of Semisupervised Clustering

et al. 2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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Summary Rejection inference aims to reduce sample bias and to improve model performance in credit scoring. We propose a semisupervised clustering approach as a new rejection inference technique. K‐prototype clustering can deal with mixed types of numeric and categorical characteristics, which are common in consumer credit data. We identify homogeneous acceptances and rejections and assign labels to part of the rejections according to the label of acceptances. We test the performance of various rejection inference methods in logit, support vector machine and random‐forests models based on data sets of real consumer loans. The predictions of clustering rejection inference show advantages over other traditional rejection inference methods. Inferring the label of the rejection from semisupervised clustering is found to help to mitigate the sample bias problem and to improve the predictive accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Inferring the Outcomes of Rejected Loans: An Application of Semisupervised Clustering

et al. 2019

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…This can be done by using some of wellknown indexes [6,21]: Calinski-Harabasz (CH), Davies -Bouldin (DB), Simplifed Silhouette Width Criterion (SWC), Hypervolume (HV) index. More compact and better separated clusters in an optimal partition will result in a greater CH index, a smaller DB index, a greater SWC index and a smaller HV index.…”

Section: Data Clusteringmentioning

confidence: 99%

Cluster analysis in retail segmentation for credit scoring

Scitovski

Šarlija

2014

Croat. Oper. Res. Rev.

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Abstract. The aim of this paper is to segment retail clients by using adaptive Mahalanobis clustering in a way that each segment can be suitable for separate credit scoring development such that a better risk assessment of retail clients could be accomplished. A real data set on retail clients from a Croatian bank was used in the paper. Grouping of the data point set is carried out by using the adaptive Mahalanobis partitioning algorithm (see, e.g., [20]). It is an incremental algorithm, which recognizes ellipsoidal clusters with the main axes in the directions of eigenvectors of the corresponding covariance matrix of the data set. On the basis of the given data set, by using the well-known DIRECT algorithm for global optimization it is possible to search successively for an optimal partition with k = 2, 3, . . . clusters. After that, a partition with the most appropriate number of clusters is determined by using various validity indexes. Based on the description of each cluster, banks could decide to develop a separate credit scoring model for each cluster as well as to create a business strategy customized to each cluster.

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“…The Fowlkes-Mallows score (FMS) [20,21] is used to compute the geometric mean of the similarity of the clusters where the ground truth labels are known where the FMS is lying between the 0 and 1, and a greater value is a better similarity among the clusters. The FMS uses the True Positives (TP), False Positive (FP), and False Negative (FN) for the similarity measure analysis is shown in Equation ( 4) [16].…”

mentioning

confidence: 99%

Probabilistic Unsupervised Machine Learning Approach for a Similar Image Recommender System for E-Commerce

Addagarla

Amalanathan

2020

Symmetry

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The recommender system is the most profound research area for e-commerce product recommendations. Currently, many e-commerce platforms use a text-based product search, which has limitations to fetch the most similar products. An image-based similarity search for recommendations had considerable gains in popularity for many areas, especially for the e-commerce platforms giving a better visual search experience by the users. In our research work, we proposed a machine-learning-based approach for a similar image-based recommender system. We applied a dimensionality reduction technique using Principal Component Analysis (PCA) through Singular Value Decomposition (SVD) for transforming the extracted features into lower-dimensional space. Further, we applied the K-Means++ clustering approach for the possible cluster identification for a similar group of products. Later, we computed the Manhattan distance measure for the input image to the target clusters set for fetching the top-N similar products with low distance measure. We compared our approach with five different unsupervised clustering algorithms, namely Minibatch, K-Mediod, Agglomerative, Brich, and the Gaussian Mixture Model (GMM), and used the 40,000 fashion product image dataset from the Kaggle web platform for the product recommendation process. We computed various cluster performance metrics on K-means++ and achieved a Silhouette Coefficient (SC) of 0.1414, a Calinski-Harabasz (CH) index score of 669.4, and a Davies–Bouldin (DB) index score of 1.8538. Finally, our proposed PCA-SVD transformed K-mean++ approach showed superior performance compared to the other five clustering approaches for similar image product recommendations.

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A New Method For Clustering In Credit Scoring Problems

Cited by 8 publications

References 24 publications

Inferring the Outcomes of Rejected Loans: An Application of Semisupervised Clustering

Inferring the Outcomes of Rejected Loans: An Application of Semisupervised Clustering

Cluster analysis in retail segmentation for credit scoring

Probabilistic Unsupervised Machine Learning Approach for a Similar Image Recommender System for E-Commerce

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