Credit Risk Assessment Using Learning Algorithms for Feature Selection

Abstract

Firefly algorithm is one of the latest outstanding bio-inspired algorithms, which could be manipulated in solving continuous or discrete optimisation problems. In this context, we have utilised the firefly algorithm accompanied by five well-known models of feature selection classifiers to have an accurate estimation of risk, and further to improve the interpret-ability of credit card prediction. One of the significant challenges in the real-world datasets is how to select features. As most of the datasets are unbalanced, the selection of features turns to the maximum class of data that is not fair. To overcome this issue, we have balanced the data using the SMOTE method. Our experimental results on four datasets show that balancing data has increased accuracy. In addition, using a hybrid firefly algorithm, the optimal combination of features that predicts the target class label is achieved. The selected features by the proposed method besides been reduced can represent both majority and minority classes.

Deep neural networks with L1 and L2 regularization for high dimensional corporate credit risk prediction

“…However, these models are suitable for scenarios with few features and potentially linear relationships. If the features are increased, the poor performance of the prediction will occur (Albu et al, 2019;Hassani et al, 2020). Therefore, machine learning algorithms that are good at processing complex data structures, are used for CCR prediction more frequently (Bhatore et al, 2020;Lappas et al, 2021;Ma et al, 2019).…”

Modelos para la evaluación de riego crediticio en el ámbito de la tecnología financiera: una revisión

“…The proposed Parallel ANNs model consists of three stages; the first stage is to create the neural network classifiers; the second stage is to integrate multiple classifiers into an ensemble output; the third stage is to perform the learning process of the PANNs model. [49] Firefly algorithm This article proposes an improvement to the firefly algorithm to solve optimization problems and feature selection.…”

Associated probabilities aggregations in multistage investment decision-making

“…Application of the AsF M operators in fuzzy MSDMM for optimal investments Authors often use heuristic approaches in their works dedicated to credit risk assessments. These include fuzzy modeling, fuzzy neural networks, machine learning, genetic programming and more (Breeden, 2021;Chang et al, 2020;De et al, 2019;Deng and Yuan, 2021;Froelich and H ajek, 2019;Hassani et al, 2021;Kamalloo and Saniee Abadeh, 2014;Santana et al, 2018;Shen et al, 2019;Shen et al, 2018;Song et al, 2020;W ojcicka-W ojtowicz and Piasecki, 2021;Wu et al, 2014;Zhang et al, 2015;Zhu et al, 2019).…”

“…, 2020; De et al. , 2019; Deng and Yuan, 2021; Froelich and Hájek, 2019; Hassani et al , 2021; Kamalloo and Saniee Abadeh, 2014; Santana et al. , 2018; Shen et al ., 2019; Shen et al.…”

“…(2019). In Hassani et al ’s (2021) study, novel credit risk assessment learning algorithms for the feature selection are developed. Using machine learning techniques, multi-stage credit rating prediction methodologies are constructed in Kamalloo and Saniee Abadeh (2014), Wu et al.…”