Article2017

A feature selection enabled hybrid‐bagging algorithm for credit risk evaluation

Shashi Dahiya · S. S. Handa · Netra Pal Singh

43 citations0/0 files storedExpert Systems

No readable copy stored yet — this record is metadata only.

No open-access location is known for this record yet.

Literature note Cite in a project Citation map

Financial Distress and Bankruptcy Prediction Data Mining and Machine Learning Applications Imbalanced Data Classification Techniques

Abstract

Abstract Hybrid models based on feature selection and machine learning techniques have significantly enhanced the accuracy of standalone models. This paper presents a feature selection‐based hybrid‐bagging algorithm (FS‐HB) for improved credit risk evaluation. The 2 feature selection methods chi‐square and principal component analysis were used for ranking and selecting the important features from the datasets. The classifiers were built on 5 training and test data partitions of the input data set. The performance of the hybrid algorithm was compared with that of the standalone classifiers: feature selection‐based classifiers and bagging. The hybrid FS‐HB algorithm performed best for qualitative dataset with less features and tree‐based unstable base classifier. Its performance on numeric data was also better than other standalone classifiers, whereas comparable to bagging with only selected features. Its performance was found better on 70:30 data partition and the type II error, which is very significant in risk evaluation was also reduced significantly. The improved performance of FS‐HB is attributed to the important features used for developing the classifier thereby reducing the complexity of the algorithm and the use of ensemble methodology, which added to the classical bias variance trade‐off and performed better than standalone classifiers.

Paper brief

Loading…

Cite this work

Citation unavailable for this work.

BibTeX RIS CSL-JSON

No public copy is stored for this record, so only the metadata is cited.

Citations in this library

Citation map →

Stored only when both papers are in the corpus — 43 citations exist upstream.

References

1998Forecasting with artificial neural networks:1997Statistical Classification Methods in Consumer Credit Scoring: A Review 2000Neural network credit scoring models 2003Credit rating analysis with support vector machines and neural networks: a market comparative study 2006Credit scoring with a data mining approach based on support vector machines 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers 2007Using neural network ensembles for bankruptcy prediction and credit scoring 2013Genetic algorithm-based heuristic for feature selection in credit risk assessment

Cited by

2022Enterprise credit risk prediction using supply chain information: A decision tree ensemble model based on the differential sampling rate, Synthetic Minority Oversampling Technique and AdaBoost 2022Credit risk evaluation: a comprehensive study 2024Cost-sensitive stacking ensemble learning for company financial distress prediction 2019Knowledge mapping of credit risk research: scientometrics analysis using CiteSpace 2021

Explore the map →

Papers that cite the same references — the relational fallback while embeddings are still backfilling.

Imported from OpenAlex (CC0) — nothing here is generated.