Feature Selection using Optimized Multiple Rank Score Model for Credit Scoring

We study the problem of discovering functional dependencies (FD) from a noisy data set. We adopt a statistical perspective and draw connections between FD discovery and structure learning in probabilistic graphical models. We show that discovering FDs from a noisy data set is equivalent to learning the structure of a model over binary random variables, where each random variable corresponds to a functional of the data set attributes. We build upon this observation to introduce FDX a conceptually simple framework in which learning functional dependencies corresponds to solving a sparse regression problem. We show that FDX can recover true functional dependencies across a diverse array of realworld and synthetic data sets, even in the presence of noisy or missing data. We find that FDX scales to large data instances with millions of tuples and hundreds of attributes while it yields an average F 1 improvement of 2× against state-of-the-art FD discovery methods.

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A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

Jin

Liu

Zhang

et al. 2021

IFS

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With the advancement of machine learning, credit scoring can be performed better. As one of the widely recognized machine learning methods, ensemble learning has demonstrated significant improvements in the predictive accuracy over individual machine learning models for credit scoring. This study proposes a novel multi-stage ensemble model with multiple K-means-based selective undersampling for credit scoring. First, a new multiple K-means-based undersampling method is proposed to deal with the imbalanced data. Then, a new selective sampling mechanism is proposed to select the better-performing base classifiers adaptively. Finally, a ne1 w feature-enhanced stacking method is proposed to construct an effective ensemble model by composing the shortlisted base classifiers. In the experiments, four datasets with four evaluation indicators are used to evaluate the performance of the proposed model, and the experimental results prove the superiority of the proposed model over other benchmark models.

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Feature Selection using Optimized Multiple Rank Score Model for Credit Scoring

Cited by 3 publications

References 18 publications

Wrapper Feature Selection for Poverty Level Prediction Based on E-Commerce Dataset

Wrapper Feature Selection for Poverty Level Prediction Based on E-Commerce Dataset

A Statistical Perspective on Discovering Functional Dependencies in Noisy Data

A novel multi-stage ensemble model with multiple K-means-based selective undersampling: An application in credit scoring

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