<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Machine Learning is a constantly growing area which has the capacity to analyze massive amounts of data and find relevant patterns, a very important feature in the era of big data. It has a wide range of application areas, including the financial field, and proved to be efficient in solving various problems, including the prediction of the default probability of a customer to meet their obligations to the bank, using classification algorithms. Their output is further used when deciding whether to approve a loan or no, based on the previous behavior of the customers, hence reduces the loss of the bank. Even though Machine Learning algorithms proved to be efficient in solutioning this type of problems, none was identified for remarkable results. This paper studies 10 different methods applied on the same dataset (Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Kernel Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, Bagging Classifier, Linear Discriminant Analysis, Neural Network - Multi Layer Perceptron) and performs a comparative analysis aiming to identify the one which outperforms the others. Their performance is evaluated based on some well-known statistical measures such as Accuracy, Misclassification Rate, Precision and Specificity. In addition, this paper also presents and evaluates the impact of feature selection on the overall performance of an algorithm.</p>
</abstract>

Machine Learning is a constantly growing area which has the capacity to analyze massive amounts of data and find relevant patterns, a very important feature in the era of big data. It has a wide range of application areas, including the financial field, and proved to be efficient in solving various problems, including the prediction of the default probability of a customer to meet their obligations to the bank, using classification algorithms. Their output is further used when deciding whether to approve a loan or no, based on the previous behavior of the customers, hence reduces the loss of the bank. Even though Machine Learning algorithms proved to be efficient in solutioning this type of problems, none was identified for remarkable results. This paper studies 10 different methods applied on the same dataset (Logistic Regression, K-Nearest Neighbor, Support Vector Machine, Kernel Support Vector Machine, Naïve Bayes, Decision Tree, Random Forest, Bagging Classifier, Linear Discriminant Analysis, Neural Network - Multi Layer Perceptron) and performs a comparative analysis aiming to identify the one which outperforms the others. Their performance is evaluated based on some well-known statistical measures such as Accuracy, Misclassification Rate, Precision and Specificity. In addition, this paper also presents and evaluates the impact of feature selection on the overall performance of an algorithm.

A comparative analysis of classification algorithms for consumer credits

Proceedings of the International Conference on Business Excellence

This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"A comparative analysis of classification algorithms for consumer credits"}

Machine Learning is a constantly growing area which has the capacity to analyze massive amounts of data and find relevant patterns, a very important feature in...

A comparative analysis of classification algorithms for consumer credits

Publié en ligne: 31 déc. 2021

Pages: 245 - 256

DOI: https://doi.org/10.2478/picbe-2021-0024

Mots clés
Machine Learning, Banking, Consumer Credits, Feature Selection, Algorithms Assessment

© 2021 Claudia Antal-Vaida, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

A comparative analysis of classification algorithms for consumer credits

Publié en ligne: 31 déc. 2021

Pages: 245 - 256

DOI: https://doi.org/10.2478/picbe-2021-0024

Mots clésMachine Learning, Banking, Consumer Credits, Feature Selection, Algorithms Assessment

© 2021 Claudia Antal-Vaida, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Machine Learning, Banking, Consumer Credits, Feature Selection, Algorithms Assessment