Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring

Hoffmann, Frank; Baesens, Bart; Martens, Jurgen; Put, Ferdinand; Vanthienen, Jan

doi:10.1002/int.10052

Cited by 35 publications

(3 citation statements)

References 21 publications

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“…Hoffmann et al [5] used ACO to predict credit rating of customers in various datasets and their results show that ACO produces the rules that yield 71.9% prediction accuracy. Parpinelli et al [19] proposed the ACO-based data miner for inducting rules from data.…”

Section: Related Workmentioning

confidence: 99%

Ant Colony Optimization, Genetic Programming and a hybrid approach for credit scoring: A comparative study

Aliehyaei

2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

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Credit scoring is a commonly used method for evaluating the risk involved in granting credits. Both Genetic Programming (GP) and Ant Colony Optimization (ACO) have been investigated in the past as possible tools for credit scoring. This paper reports an investigation into the relative performances of GP, ACO and a new hybrid GP-ACO approach, which relies on the ACO technique to produce the initial populations for the GP technique. Performance of the hybrid approach has been compared with both the GP and ACO approaches using two well-known benchmark data sets. Experimental results demonstrate the dependence of GP and ACO classification accuracies on the input data set. For any given data set, the hybrid approach performs better than the worse of the other two methods. Results also show that use of ACO in the hybrid approach has only a limited impact in improving GP performance.

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Section: Related Workmentioning

confidence: 99%

Ant Colony Optimization, Genetic Programming and a hybrid approach for credit scoring: A comparative study

Aliehyaei

2014

The 8th International Conference on Software, Knowledge, Information Management and Applications (SKIMA 2014)

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“…It has been found that in most cases the ensembles produce more accurate predictions than the base classifiers (Dietterich, 1997). Hoffmann, Baesens, Martens, Put, and Vanthienen, 2002) reported that the boosted genetic fuzzy classifier performed better than both the neuro fuzzy classifier and C4.5 algorithm. (West et al, 2005) reported that ensemble model of NNs obtained the higher accuracy than the single NN in credit risk evaluation and bankruptcy prediction.…”

Section: Introductionmentioning

confidence: 99%

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

2017

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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.

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“…Researchers have shown that aggregating approach can easily achieve improved accuracies by an aggregation of individual classifiers for credit scoring as well as the classification application. (Hoffmann et al, 2002) reported that the boosted genetic fuzzy classifier performed better than both the neuro fuzzy classifier and C4.5 algorithm. (West (2005)) reported that ensemble model of NNs obtained the higher accuracy than the single NN in credit scoring and bankruptcy prediction.…”

Section: Introductionmentioning

confidence: 99%