Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring

Zhang, Runchi; Qiu, Zhiyi

doi:10.1371/journal.pone.0234254

Cited by 15 publications

(13 citation statements)

References 78 publications

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“…Random Forest (RF): This algorithm utilizes multiple decision trees where each tree uses different randomly selected features to split nodes which makes RF robust with respect to noise and over-fitting [8], [76]. RF uses tree classifiers where the generalization error depends on the overall performance of each tree on its own and the correspondence between them [8].…”

Section: Review Of Some ML Algorithmsmentioning

confidence: 99%

“…Using a random selection of features to split nodes makes it robust with respect to noise [8] and over-fitting [76]. RF algorithm is a popular algorithm that has been used not only as a classifier but also as an algorithm for selecting and figuring out which features are of more importance.…”

Section: Review Of Some ML Algorithmsmentioning

confidence: 99%

“…Also, RF has been used as means to build optimization algorithms that are used for hyper-parameter tuning such as Sequential Model-based Algorithm Configuration (SMAC). Not only RF algorithm randomize the selection of features subset to be used within a tree, but it also applies randomization to the training set to be used in the individual trees [76]. Table 2.3 summarizes the important HPs for RF algorithm and highlights their importance.…”

Section: Review Of Some ML Algorithmsmentioning

confidence: 99%

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Data Balancing and Hyper-parameter Optimization for Machine Learning Algorithms for Secure IoT Networks

Elghalhoud

Naik

Zaman

et al. 2022

Proceedings of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks on 18th ACM Internatio

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Nowadays, many industries rely on Machine Learning (ML) algorithms and their ability to learn from existing data to make inferences about new unlabeled data. Applying ML algorithms to the network security domain is not new. However, without proper data preprocessing and proper optimization of the hyper-parameters (HPs) of ML algorithms, these algorithms might not achieve their full potential. Furthermore, attacks on network infrastructures come in a variety of forms and at different frequencies. Cyber-security experts often require the help of an automated process that filters and classifies attacks. To apply specific preventive measures for securing networks, the classification of the attack type is key. Many ML models have been proposed as a base for Network Intrusion Detection (NID) systems. However, their performance varies based on multiple factors. For instance, an ML model fitted on a highly imbalanced dataset may be biased toward over-represented attack types. On the other hand, paying attention only to the ML model's performance in the minority classes can negatively affect its performance in the majority classes or overall performance. This research proposes a framework that applies pre-processing steps, including data balancing, and utilizes optimization techniques to tune the HPs of random forest, gradient boosting machine, and deep neural networks. The conducted experiments in this research provide a performance comparison between three different optimization algorithms: Tree-structured Parzen Estimator (TPE), Bayesian Optimization and Hyperband (BOHB), and Particle Swarm Optimization (PSO). The research results show that through data balancing and optimization of the HPs and architecture of deep neural networks, their performance can improve significantly: false alarm rate of 0% and only 1.79% using the BoT-IoT and the ToN-IoT benchmark datasets, respectively.To address the issue of imbalanced datasets, this research gives a data balancing algorithm and compares its performance to other existing approaches that use: Random Over-Sampling (ROS), Synthetic Minority Oversampling TEchnique (SMOTE), Adaptive Synthetic Sampling (ADASYN), and Generative Adversarial Networks (GAN). The data balancing algorithm is combined with Convolutional Neural Networks (CNN) to extract spatial features and classify the different attack types. Using the NSL-KDD and the BoT-IoT datasets for benchmarking, the proposed system achieves high performance in the minority classes: recall scores of 70.50% and 72.08% on the User to Root (U2R) and Remote to Local (R2L) attack classes of the NSL-KDD dataset, respectively, while maintaining an overall False Alarm Rate (FAR) of 6.50% and a recall of 90.46% on the binary classification task. The proposed system scores a weighted average F1-Score of 99.45% on the multi-class classification task using the BoT-IoT dataset.

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Section: Review Of Some ML Algorithmsmentioning

confidence: 99%

Section: Review Of Some ML Algorithmsmentioning

confidence: 99%

Section: Review Of Some ML Algorithmsmentioning

confidence: 99%

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Data Balancing and Hyper-parameter Optimization for Machine Learning Algorithms for Secure IoT Networks

Elghalhoud

Naik

Zaman

et al. 2022

Proceedings of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks on 18th ACM Internatio

View full text Add to dashboard Cite

show abstract

“…4) The Area Under the ROC Curve (AUC) measures the classification ability of entire sample and the balance of classified samples simultaneously [57]. Thus, it can be considered as more appropriate measurement in an imbalanced credit scoring [41].…”

Section: Performance Measuresmentioning

confidence: 99%

“…However, MHS-RF unsuccessfully detects default customers as well as the models provided by NNBag [21] and CS-Bagging-CS-CART [62]. Compared with BP-ANN-PSO [57], the CS-NNE model can compromise the accuracy between classes better than BP-ANN-PSO, as indicated by GM. These models will generate a large number of NPLs.…”

Section: ) Performance Comparison With Previous Studiesmentioning

confidence: 99%

A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

2021

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Most existing studies on credit scoring adapted a concept of classifier ensemble for solving an imbalanced dataset. They apply resampling methods to generate multiple training subsets for constructing multiple base classifiers. However, this approach leads to several problems that degrade the classification performance, such as problems of information loss, model overfitting, and computational cost. Thus, we propose a novel ensemble approach for developing a credit scoring model based on a cost-sensitive neural network, called Cost-sensitive Neural Network Ensemble (CS-NNE). In the proposed approach, multiple class weights are adapted to original training data, enabling the multiple base neural networks to consider imbalanced classes. Following this approach, a high diversity of multiple base classifiers without consequent problems can be achieved. The approach's effectiveness is evaluated on five real-world credit datasets. Among them is a loan-requesting dataset provided by a financial institution in Thailand. The remaining datasets are publicly available and widely used by several existing studies. The experimental results showed that the proposed CS-NNE approach improves the predictive performance over a single neural network based on imbalanced credit datasets, e.g., Thai credit dataset, by achieving 1.36%, 15.67%, and 6.11% Area under the ROC Curve (AUC), Default Detection Rate (DDR), and G-Mean (GM), respectively, and achieving the best Misclassification Cost (MC). The proposed CS-NNE approach can effectively solve a class of imbalance problems and outperform many existing models. The prediction model can well compromise between classes of default (bad credit applicants) and non-default (good credit applicants), whereas existing approaches preferred a class of non-default over default loans (having high specificity and low DDR), resulting in NPL.

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Intelligent credit scoring using deep learning methods

Gicić

Ðonko

Subaşı

2023

Concurrency and Computation

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Summary Credit scoring is one the most important parts of credit risk management in reducing the risk of client defaults and bankruptcies. Deep learning has received much attention in recent years, but it has not been implemented so intensively in credit scoring compared to other financial domains. In this article, stacked unidirectional and bidirectional LSTM (long short‐term memory) networks as a complex area of deep learning are applied in solving credit scoring problems for the first time. The proposed robust model exploits the full potential of the three‐layer stacked LSTM and BDLSTM (bidirectional LSTM) architecture with the treatment and modeling of public datasets in a novel way since credit scoring is not a time sequence problem. Attributes of each loan instance were transformed into a sequence of the matrix with a fixed sliding window approach with a one‐time step. Our proposed models outperform existing and much more complex deep learning solutions thus we succeeded in preserving simplicity. In this article, measures of different types are employed to carry out consistent conclusions. The results by applying three hidden layers on the German Credit dataset showed an accuracy of 87.19%, for Kaggle dataset accuracy reached 93.69%, and for Microcredit dataset accuracy of 97.80%.

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Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring

Cited by 15 publications

References 78 publications

Data Balancing and Hyper-parameter Optimization for Machine Learning Algorithms for Secure IoT Networks

Data Balancing and Hyper-parameter Optimization for Machine Learning Algorithms for Secure IoT Networks

A Novel Method for Credit Scoring Based on Cost-Sensitive Neural Network Ensemble

Intelligent credit scoring using deep learning methods

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