Zeitschriftenartikel: „Naive credal classifier“

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Zaffalon, Marco. „The naive credal classifier“. Journal of Statistical Planning and Inference 105, Nr. 1 (Juni 2002): 5–21. http://dx.doi.org/10.1016/s0378-3758(01)00201-4.

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2

Antonucci, Alessandro, und Giorgio Corani. „The multilabel naive credal classifier“. International Journal of Approximate Reasoning 83 (April 2017): 320–36. http://dx.doi.org/10.1016/j.ijar.2016.10.006.

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3

ABELLÁN, JOAQUÍN, und ANDRÉS R. MASEGOSA. „IMPRECISE CLASSIFICATION WITH CREDAL DECISION TREES“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, Nr. 05 (Oktober 2012): 763–87. http://dx.doi.org/10.1142/s0218488512500353.

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In this paper, we present the following contributions: (i) an adaptation of a precise classifier to work on imprecise classification for cost-sensitive problems; (ii) a new measure to check the performance of an imprecise classifier. The imprecise classifier is based on a method to build simple decision trees that we have modified for imprecise classification. It uses the Imprecise Dirichlet Model (IDM) to represent information, with the upper entropy as a tool for splitting. Our new measure to compare imprecise classifiers takes errors into account. Thus far, this has not been considered by other measures for classifiers of this type. This measure penalizes wrong predictions using a cost matrix of the errors, given by an expert; and it quantifies the success of an imprecise classifier based on the cardinal number of the set of non-dominated states returned. To compare the performance of our imprecise classification method and the new measure, we have used a second imprecise classifier known as Naive Credal Classifier (NCC) which is a variation of the classic Naive Bayes using the IDM; and a known measure for imprecise classification.

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Moral-García, Serafín, Javier G. Castellano, Carlos J. Mantas und Joaquín Abellán. „Using extreme prior probabilities on the Naive Credal Classifier“. Knowledge-Based Systems 237 (Februar 2022): 107707. http://dx.doi.org/10.1016/j.knosys.2021.107707.

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5

Abellán, Joaquín. „Application of uncertainty measures on credal sets on the naive Bayesian classifier“. International Journal of General Systems 35, Nr. 6 (Dezember 2006): 675–86. http://dx.doi.org/10.1080/03081070600867039.

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6

Zhao, B., M. Yang, H. R. Diao, B. An, Y. C. Zhao und Y. M. Zhang. „A novel approach to transformer fault diagnosis using IDM and naive credal classifier“. International Journal of Electrical Power & Energy Systems 105 (Februar 2019): 846–55. http://dx.doi.org/10.1016/j.ijepes.2018.09.029.

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7

Zaffalon, Marco, Keith Wesnes und Orlando Petrini. „Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data“. Artificial Intelligence in Medicine 29, Nr. 1-2 (September 2003): 61–79. http://dx.doi.org/10.1016/s0933-3657(03)00046-0.

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8

ABELLÁN, JOAQUÍN, und ANDRÉS R. MASEGOSA. „A FILTER-WRAPPER METHOD TO SELECT VARIABLES FOR THE NAIVE BAYES CLASSIFIER BASED ON CREDAL DECISION TREES“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 17, Nr. 06 (Dezember 2009): 833–54. http://dx.doi.org/10.1142/s0218488509006297.

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Variable selection methods play an important role in the field of attribute mining. In the last few years, several feature selection methods have appeared showing that the use of a set of decision trees learnt from a database can be a useful tool for selecting relevant and informative variables regarding a main class variable. With the Naive Bayes classifier as reference, in this article, our aims are twofold: (1) to study what split criterion has better performance when a complete decision tree is used to select variables; and (2) to present a filter-wrapper selection method using decision trees built with the best possible split criterion obtained in (1).

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Chen, Yihong. „Credit card customers churn prediction by nine classifiers“. Applied and Computational Engineering 48, Nr. 1 (19.03.2024): 237–47. http://dx.doi.org/10.54254/2755-2721/48/20241575.

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Recently, losing credit card customers has been particularly serious. Using the found data set from kaggle website, this paper wants to help the bank manager by predicting for them to identify the customers who are likely to leave, so they can approach them in advance to offer them better services and sway their decisions. Nine classifiers are used to carry out model training and evaluation and finally develop credit card customers churn prediction. AdaBoost, XGBoost, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors, Support Vector Classifier, and Logistic Regression are the nine classifiers. The result shows that the credit card customer churn model can be predicted by all machine learning models. Among them, the XGBoost model performs exceptionally well, with a training accuracy of 100%, a test accuracy of 97%, and the highest F1 score of 92%. So it can be concluded that this model can be applied to relevant datasets for prediction in order to assist banks in better retaining their existing customers.

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Takawira, Oliver, und John W. Muteba Mwamba. „DETERMINANTS OF SOVEREIGN CREDIT RATINGS: AN APPLICATION OF THE NAÏVE BAYES CLASSIFIER“. Eurasian Journal of Economics and Finance 8, Nr. 4 (2020): 279–99. http://dx.doi.org/10.15604/ejef.2020.08.04.008.

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This is an analysis of South Africa’s (SA) sovereign credit rating (SCR) using Naïve Bayes, a Machine learning (ML) technique. Quarterly data from 1999 to 2018 of macroeconomic variables and categorical SCRs were analyzed and classified to predict and compare variables used in assigning SCRs. A sovereign credit rating (SCR) is a measurement of a sovereign government’s ability to meet its financial debt obligations. The differences by Credit Rating Agencies (CRA) on rating grades on similar firms and sovereigns have raised questions on which elements truly determine credit ratings. Sovereign ratings were split into two (2) categories that is less stable and more stable. Through data cross-validation for supervised learning, the study compared variables used in assessing sovereign rating by the major rating agencies namely Fitch, Moody’s and Standard and Poor’s. Cross-validation splits the dataset into train set and test set. The research applied cross-validation to reduce the effects of overfitting on the Naïve Bayes Classification model. Naïve Bayes Classification is a Machine-learning algorithm that utilizes the Bayes theorem in classification of objects by following a probabilistic approach. All variables in the data were split in the ratio of 80:20 for the train set and test set respectively. Naïve Bayes managed to classify the given variables using the two SCR categories that is more stable and less stable. Variables classified under more stable indicates that ratings are high or favorable and those for less stable show unfavorable or low ratings. The findings show that CRAs use different macroeconomic variables to assess and assign sovereign ratings. Household debt to disposable income, exchange rates and inflation were the most important variables for estimating and classifying ratings.

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Yang, Zhen. „Utilization of Quantization Method on Credit Risk Assessment“. Applied Mechanics and Materials 472 (Januar 2014): 432–36. http://dx.doi.org/10.4028/www.scientific.net/amm.472.432.

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Credit risk assessment is critical factor in credit risk management, which has played a key role in financial and banking industry. Many classification methods are used in credit risk assessment aiming to establish classifiers to predict the credit state of the corporate (good or bad). However, most of classification methods can not handle continuous variables. So, continuous variables must be quantified. In this paper, we first propose an improved quantization method, namely IDM, based on the statistical independence; then we use data mining techniques, i.e., C4.5 decision tree, Naive-Bayes and SVM classifier, to classify and predict the quantified credit data. The aim is to investigate the effect of quantization method on the classification of credit approval data. The Experimental results show that our approach significantly improves the mean accuracy of classification than other known quantization methods. This denotes that the proposed method can make an effective interpretation and point out the ability of design of a new intelligent assistance credit approval data system.

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., S. B. Siledar. „COMPARATIVE ANALYSIS OF NAIVE BAYS CLASSIFIER AND DECISION TREE C4.5 ON CREDIT PAYMENT DATA SET“. International Journal of Research in Engineering and Technology 06, Nr. 04 (25.04.2017): 43–44. http://dx.doi.org/10.15623/ijret.2017.0604010.

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Farrales, Victorino, Jonnifer Mandigma, Casielyn Capistrano, Severino Bedis, Jr. und Aleta Fabregas. „Credit Assessment and Recommendation System (CARS) using Naive Bayesian Algorithm“. Technologique: A Global Journal on Technological Developments and Scientific Innovations 2, Nr. 1 (31.08.2024): 61–69. http://dx.doi.org/10.62718/vmca.tech-gjtdsi.2.1.sc-0724-015.

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With the increasing demand for credit services to fuel economic growth, traditional credit assessment methods, relying on outdated regulations and manual evaluations, hinder efficiency. The proposed Credit Assessment and Recommendation System aims to revolutionize this process by using a Naïve Bayes Classifier to swiftly and accurately determine credit eligibility. By analyzing customers' biographic, demographic, and historical data, the system can predict approval outcomes and provide actionable recommendations. This approach promises to streamline the credit application process, reduce risks associated with manual assessments, and achieve a prediction accuracy of 90%.

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Khandale, Shreyas, Prathamesh Patil und Rohan Patil. „Predicting Credit Card Defaults with Machine Learning“. International Journal for Research in Applied Science and Engineering Technology 11, Nr. 10 (31.10.2023): 33–41. http://dx.doi.org/10.22214/ijraset.2023.55934.

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Abstract: This research paper focuses on the application of machine learning techniques to predict credit card defaults. The study utilizes a comprehensive dataset comprising diverse features related to credit card usage and payment behavior. By leveraging this dataset, the research aims to develop and evaluate predictive models using two popular machine learning algorithms: Logistic Regression and Naive Bayes classifiers. In addition to the model implementation and evaluation, the research incorporates exploratory data analysis techniques to gain deeper insights into the dataset. Exploratory data analysis involves visualizing and analyzing key patterns, trends, and relationships within the dataset. By combining predictive modeling and exploratory analysis, this research aims to provide a comprehensive understanding of credit card default prediction, thereby assisting financial institutions in making more informed decisions. The implementation of Logistic Regression and Naive Bayes classifiers allows for a comparison of the performance of these two popular algorithms in predicting credit card defaults. Logistic Regression is a widely used algorithm known for its interpretability and robustness, while Naive Bayes is based on probabilistic principles and is known for its simplicity and efficiency. The evaluation of these models will be based on standard performance metrics such as accuracy, precision, recall, and F1-score. Furthermore, the research employs exploratory data analysis techniques to uncover valuable insights within the dataset. Through visualizations and statistical analysis, this analysis aims to identify correlations, trends, and anomalies that may contribute to credit card defaults. Exploratory data analysis can help uncover hidden patterns and provide valuable contextual information, enhancing the understanding of the underlying factors associated with credit card defaults.

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Antika, Dwi Putri, Mohamat Fatekurohman und I. Made Tirta. „Banking Credit Risk Analysis with Naive Bayes Approach and Cox Proportional Hazard“. International Journal of Advanced Engineering Research and Science 9, Nr. 8 (2022): 365–70. http://dx.doi.org/10.22161/ijaers.98.41.

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Credit is needed for some people for certain purposes. In credit, it takes a party that can be used as an intermediary such as a bank. The debtor may not be able to make payments according to the original policy or even cause losses where the Bank may lose the opportunity to earn interest, causing a decrease in total income. This problem is included in the case of non-performing loans. In statistics, the duration of time between a person not making a payment on time until a non-current loan occurs can be predicted using survival analysis. Meanwhile, to predict credit status, you can use classification or prediction methods in machine learning to find out how much influence the predictor variable has. In this study, with a different case, focusing on the credit risk case of how a bank decides to provide credit to prospective debtors using the classifier method found in Machine Learning, namely Naive Bayes and Cox regression from survival analysis. Through the evaluation test of the naive bayes classifier model using accuracy values, confusion matrix and ROC, it can be concluded that this model is a model with good performance for predicting credit status. Multinomial nave Bayes in this study has a higher performance value than Gaussian Naïve Bayes and Bernoulli Naïve Bayes which is 92%. Through cox regression, it is obtained that income factors and loan history have a major influence on determining credit status.

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Prasetya, Ichwanul Kahfi, Devi Putri Isnawarty, Abdullah Fahmi, Salman Alfarizi Pradana Andikaputra und Wibawati Wibawati. „Comparing the Performance of Multivariate Hotelling’s T2 Control Chart and Naive Bayes Classifier for Credit Card Fraud Detection“. Inferensi 7, Nr. 1 (25.03.2024): 11. http://dx.doi.org/10.12962/j27213862.v7i1.18755.

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Gade, Prof S. P. „Credit Risk Analysis Using Naive Bayes in Machine Learning“. International Journal for Research in Applied Science and Engineering Technology 11, Nr. 5 (31.05.2023): 5588–92. http://dx.doi.org/10.22214/ijraset.2023.52943.

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Abstract: A method of data analysis called machine learning automates the creation of analytical models. It is an area of artificial intelligence based on the idea that robots can learn from data, recognise patterns, and form opinions with little help from people. One of the most important and difficult tasks facing banks is the evaluation of credit risk. To prevent financial loss, accuracy is crucial in the classification of credit data. Lending money to people who need it is a major operation in the banking sector. The depositor bank receives the interest earned by the main borrowers in order to repay the principal borrowed from them. Financial risk management is increasingly focusing on credit risk analysis. The ability to estimate credit risk, monitor it, trust the model, and process loans well are essential for decision-making and transparency. In this study, we develop binary classifiers based on machine learning and deep learning models on actual data to forecast the likelihood of loan default. In the aforementioned research, we examine various methods for credit risk analysis that are used to the assessment of credit risk datasets.

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Qasem, Mais Haj, und Loai Nemer. „Extreme Learning Machine for Credit Risk Analysis“. Journal of Intelligent Systems 29, Nr. 1 (18.06.2018): 640–52. http://dx.doi.org/10.1515/jisys-2018-0058.

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Abstract Credit risk analysis is important for financial institutions that provide loans to businesses and individuals. Banks and other financial institutions generally face risks that are mostly of financial nature; hence, such institutions must balance risks and returns. Analyzing or determining risk levels involved in credits, finances, and loans can be performed through predictive analytic techniques, such as an extreme learning machine (ELM). In this work, we empirically evaluated the performance of an ELM for credit risk problems and compared it to naive Bayes, decision tree, and multi-layer perceptron (MLP). The comparison was conducted on the basis of a German credit risk dataset. The simulation results of statistical measures of performance corroborated that the ELM outperforms naive Bayes, decision tree, and MLP classifiers by 1.8248%, 16.6346%, and 5.8934%, respectively.

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Niloy, NH. „Naïve Bayesian Classifier and Classification Trees for the Predictive Accuracy of Probability of Default Credit Card Clients“. American Journal of Data Mining and Knowledge Discovery 3, Nr. 1 (2018): 1. http://dx.doi.org/10.11648/j.ajdmkd.20180301.11.

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Damanik, Joel Rayapoh, Rahmat Fauzi und Faqih Hamami. „Implementasi Algoritma Klasifikasi Naïve Bayes Untuk Klasifikasi Credit Scoring Pada Platform Peer-To-Peer Lending“. Journal of Computer System and Informatics (JoSYC) 4, Nr. 4 (25.08.2023): 880–90. http://dx.doi.org/10.47065/josyc.v4i4.4059.

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Di Indonesia, seiring dengan pertumbuhan fintetch mengenai P2P lending, juga timbul P2P lending ilegal yang beroperasi tanpa izin dari otoritas berwenang. Diperkirakan terdapat sekitar 400 perusahaan fintech ilegal yang beroperasi di Indonesia. Masalah umum yang dihadapi oleh platform P2P lending adalah ketidakmampuannya mengantisipasi pembayaran yang gagal oleh peminjam. Hal ini disebabkan oleh tingginya suku bunga yang diterapkan dan kurangnya seleksi terhadap peminjam dengan risiko kredit yang tinggi atau rendah. Untuk mengatasi masalah ini, diperlukan pengembangan model sistem Machine Learning yang dapat mengklasifikasikan data peminjam dan terintegrasi dengan informasi dari lembaga keuangan lainnya. Tujuannya adalah untuk menyaring calon peminjam dengan risiko kredit yang tinggi atau rendah. Salah satu model yang digunakan adalah algoritma Naïve Bayes Classifier. Metode ini berdasarkan teorema Bayes dan merupakan algoritma yang memproses klasifikasi sederhana dengan independensi variabel. Penggunaan algoritma Naïve Bayes Classifier diharapkan dapat menciptakan sistem yang membantu platform P2P lending dalam seleksi calon peminjam. Model ini akan memprediksi risiko kredit rendah atau tinggi bagi pengguna atau pelanggan P2P lending. Untuk mencapai performa klasifikasi optimal, dilakukan tuning hyperparameter pada setiap simulasi. Hyperparameter tuning adalah proses mencari nilai terbaik untuk parameter dalam model machine learning guna meningkatkan performa. Pada algoritma GaussianNB, parameter yang dituning adalah var_smoothing. Hasil tuning hyperparameter terbaik ditemukan dengan nilai var_smoothing sebesar 0.009638958856642498, dengan pembagian train_size dan test_size sebesar 70:30. Dengan konfigurasi ini, model mencapai tingkat akurasi sebesar 95%.

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Wu, Xian, und Huan Liu. „Application of Big Data Unbalanced Classification Algorithm in Credit Risk Analysis of Insurance Companies“. Journal of Mathematics 2022 (25.03.2022): 1–10. http://dx.doi.org/10.1155/2022/3899801.

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The 2008 global financial crisis triggered by subprime mortgage crisis in the United States and the ongoing European debt crisis have urged governments and academics to pay high attention to financial industry risk supervision. The financial industry has actively implemented comprehensive risk management. As an important component of the financial industry, the insurance industry implements comprehensive risk management to control the risks of insurance companies. Propose an integrated learning model based on imbalanced dataset resampling and apply it to UCI dataset (University of California Irvine). First, resampling technology is used to preprocess the unbalanced dataset to obtain a relatively balanced training set. Then, use the classic backpropagation neural network, classic k-nearest neighbor, and classic Naive Bayes three algorithms as the base classifier and use the Bagging strategy to get the ensemble learning model. In order to verify its effectiveness, F-measure and G-mean methods are used to measure the performance of the classifier. The subject mainly focuses on the classification of relevance vector machine (RVM) in two types of large-scale datasets, imbalanced and balanced, and proposes solutions for these two types of data. This explains the effectiveness of the disequilibrium classification algorithm used in the risk analysis of insurance companies.

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Wu, Jindi. „Comparison of machine learning algorithms for credit card fraud transaction prediction“. Applied and Computational Engineering 6, Nr. 1 (14.06.2023): 1475–84. http://dx.doi.org/10.54254/2755-2721/6/20230934.

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Nowadays, credit card transaction is widely used in commercial activities, the number of fraud transaction is increasing. Especially when e-commerce and online shopping have arisen explosively and COVID-19 pandemic spread, the fraud transaction increasing surprisingly. Detecting the credit card fraud transaction is of great importance for both banks and depositors. To verify the effectiveness of machine learning algorithms for this task, several models are leveraged and examined in this work. These models include naive Gaussian bayes model, logistic regression model, random forest classifier, convolutional neural network (CNN) and support vector machine (SVM). Besides, the effectiveness of pre-processing techniques, including undersampling and oversampling, is also conducted on the training dataset for validating their effectiveness on predicting whether a transaction is a fraud or not and their joint effort with different models. In this work, these models are implemented with European dataset of credit card fraud and the best method is selected for this application.

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Shimu Khatun, Mst, Bhuiyan Rabiul Alam, Md Taslim und Md Alam Hossain. „Handling Class Imbalance in Credit Card Fraud Using Various Sampling Techniques“. American Journal of Multidisciplinary Research and Innovation 1, Nr. 4 (03.10.2022): 160–68. http://dx.doi.org/10.54536/ajmri.v1i4.633.

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Over the last few decades, credit card fraud (CCF) has been a severe problem for both cardholders and card providers. Credit card transactions are fast expanding as internet technology advances, significantly relying on the internet. With advanced technology and increased credit card usage, fraud rates are becoming a problem for the economy. However, the credit card dataset is highly imbalanced and skewed. Many classification techniques are used to classify fraud and non-fraud but in a certain condition, they may not generate the best results. Different types of sampling techniques such as under-over sampling, Synthetic Minority Oversampling, and Adaptive synthetic techniques have been used to overcome the class imbalance problem in the credit card dataset. Then, the sampled datasets are classified using different machine learning techniques like Decision Tree, Random Forest, K-Nearest Neighbors, Logistic Regression, and Naive Bayes. Recall, F1- score, accuracy, precision, and error rate used to evaluate the model performance. The Logistic Regression model achieved the highest result with 99.94% after under sampling techniques and Random Forest model achieved the highest result with 99.964% after over sampling techniques.

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Kumain, Kiran. „Analysis of Fraud Detection on Credit Cards using Data Mining Techniques“. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, Nr. 1 (30.04.2020): 916–24. http://dx.doi.org/10.17762/turcomat.v11i1.13590.

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Credit cards are widely used and accepted in the financial sector all over the world. The latest trend is to use electronic payments and to go cashless. Unfortunately, these credit card-based online transactions and cashless payments invite online fraudsters, who then attack all forms of online payment, including shopping sites and banking services. According to polls, approximately 4 billion people are presently affected by credit card fraud detection, and by 2025, that figure is projected to increase to 8 billion. Concern for its detection has increased as a result of this worrying pace. Both research scholars and industry experts have contributed their effort in this area for this goal. When considering the credit card detection method, its detection largely becomes a difficult problem. Due mostly to its unstable nature and dependence on customer behaviour, and secondarily because the dataset is readily available and easily accessible. This causes the dataset to become imbalanced, which makes it harder for a researcher to find instances of credit card fraud. Implementations of data mining algorithms are suitable for overcoming such difficulties. As a result, applying the proposed thesis necessitates using the random forest, decision trees, logistic regression, and Naive Bayes. The paper also proposes the use of a stacking algorithm, which integrates the basic theories of decision trees, logistic regression, and random forests, in addition to datamining techniques. According to experimental study of the aforementioned classifiers, the stacking algorithm produced an optimum model with exact precisions and generated the greatest accuracy of 97.78%.

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Vikash Chander Maheshwari, Nurul Aida Osman und Norshakirah Aziz. „A Hybrid Approach Adopted for Credit Card Fraud Detection Based on Deep Neural Networks and Attention Mechanism“. Journal of Advanced Research in Applied Sciences and Engineering Technology 32, Nr. 1 (19.08.2023): 315–31. http://dx.doi.org/10.37934/araset.32.1.315331.

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Over the past few years, credit card fraud has become a serious problem as more individuals rely on credit cards for purchases. The significant increase in fraudulent activities can be attributed to advancements in technology and the prevalence of online transactions, leading to significant financial losses. To address this issue, an effective fraud detection system needs to be developed and put into practice. Machine learning techniques are commonly used to automatically detect credit card fraud, but they do not consider deceptive behaviour or behavioural issues that could lead to false alarms. The objective of this research is to determine how to identify instances of credit card fraud. This paper aimed to create a model using deep learning and SMOTE oversampling technique to anticipate credit card fraud. A Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM) and an attention mechanism is suggested for detecting fraud. This model is known to be effective for processing sequential data with complex relationships between vectors. The performance of RNN-LSTM is compared to XGBoost, Random Forest, Naive Bayes, SVM, and ANN classifiers, and the experiments indicate that our proposed model achieves high accuracy of 99.4% and produces strong results. The suggested model has the potential to decrease financial losses worldwide by identifying instances of credit card scams or frauds.

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„Swindling Shonky Anatomization of Credit Card Transactions using Machine Learning“. International Journal of Recent Technology and Engineering 8, Nr. 4 (30.11.2019): 1477–83. http://dx.doi.org/10.35940/ijrte.d7621.118419.

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With the fast moving technological advancement, the internet usage has been increased rapidly in all the fields. The money transactions for all the applications like online shopping, banking transactions, bill settlement in any industries, online ticket booking for travel and hotels, Fees payment for educational organization, Payment for treatment to hospitals, Payment for super market and variety of applications are using online credit card transactions. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the institution. With this background, this paper focuses on predicting the fraudulent credit card transaction. The Credit Card Transaction dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of fraudulent credit card transaction is achieved in four ways. Firstly, the relationship between the variables of the dataset is identified and represented by the graphical notations. Secondly, the feature importance of the dataset is identified using Random Forest, Ada boost, Logistic Regression, Decision Tree, Extra Tree, Gradient Boosting and Naive Bayes classifiers. Thirdly, the extracted feature importance if the credit card transaction dataset is fitted to Random Forest classifier, Ada boost classifier, Logistic Regression classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Naive Bayes classifier. Fourth, the Performance Analysis is done by analyzing the performance metrics like Accuracy, FScore, AUC Score, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator Integrated Development Environment. Experimental Results shows that the Decision Tree classifier have achieved the effective prediction with the precision of 1.0, recall of 1.0, FScore of 1.0 , AUC Score of 89.09 and Accuracy of 99.92%.

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„Ensemble Classification Method for Credit Card Fraud Detection“. International Journal of Recent Technology and Engineering 8, Nr. 3 (30.09.2019): 423–27. http://dx.doi.org/10.35940/ijrte.c4213.098319.

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Credit card frauds are on the rise and are getting smarter with the passage of time. Usually, fraudulent transactions are conducted by stealing the credit card. When the loss of the card is not noticed by the cardholder, a huge loss can be faced by the credit card company. In the existing work, it has been found that the researchers have utilized Voting based method to identify credit card frauds. The problem with voting based method is that they are more complex and more time consuming. In this research work, a hybrid approach based on KNN and Naive Bayes for the detection of credit card frauds. KNN will be used as the base classifier and it will return predicted result. The predicted result will be provided as input to the Naive Bayes classifier which will generate the final result. The proposed model will be compared with existing techniques and the results are analyzed in terms of recall, precision, accuracy and execution time.

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Tanza, Alifia, und Dina Tri Utari. „Comparison of the Naïve Bayes Classifier and Decision Tree J48 for Credit Classification of Bank Customers“. EKSAKTA: Journal of Sciences and Data Analysis, 29.08.2022. http://dx.doi.org/10.20885/eksakta.vol3.iss2.art2.

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The bank conducts an analysis or survey in the credit system to determine whether the customer is eligible to receive credit. With a case study of Bank BJB debtor data in December 2021, credit classification analysis was carried out by forming a model using the Naïve Bayes Classifier and Decision Tree J48. Thus it is expected to minimize the occurrence of bad loans. The data are divided into several categories: debtors with good, substandard, doubtful, and bad credit. The analysis was carried out using a 10-fold cross-validation model, where the results obtained from both tests, the highest accuracy value was the Decision Tree J48 of 78.26%. While the Naïve Bayes Classifier has a lower level of accuracy, the prediction results tend to be better than the Decision Tree J48. The prediction results with the Naïve Bayes Classifier can predict all classes and the most influential variable in classifying credit is the loan term.

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Ileberi, Emmanuel, Yanxia Sun und Zenghui Wang. „A machine learning based credit card fraud detection using the GA algorithm for feature selection“. Journal of Big Data 9, Nr. 1 (25.02.2022). http://dx.doi.org/10.1186/s40537-022-00573-8.

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AbstractThe recent advances of e-commerce and e-payment systems have sparked an increase in financial fraud cases such as credit card fraud. It is therefore crucial to implement mechanisms that can detect the credit card fraud. Features of credit card frauds play important role when machine learning is used for credit card fraud detection, and they must be chosen properly. This paper proposes a machine learning (ML) based credit card fraud detection engine using the genetic algorithm (GA) for feature selection. After the optimized features are chosen, the proposed detection engine uses the following ML classifiers: Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), Artificial Neural Network (ANN), and Naive Bayes (NB). To validate the performance, the proposed credit card fraud detection engine is evaluated using a dataset generated from European cardholders. The result demonstrated that our proposed approach outperforms existing systems.

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Tripathy, Nrusingha, Subrat Kumar Nayak, Julius Femi Godslove, Ibanga Kpereobong Friday und Sasanka Sekhar Dalai. „Credit Card Fraud Detection Using Logistic Regression and Synthetic Minority Oversampling Technique (SMOTE) Approach.“ International Journal of Computer and Communication Technology, November 2022, 38–45. http://dx.doi.org/10.47893/ijcct.2022.1438.

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Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. The most common issues in today's culture are credit card scams. This kind of fraud typically happens when someone uses someone else's credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models' reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. The effectiveness of naive bayes, k-nearest neighbour, and logistic regression on highly skewed credit card fraud data is examined in this research. The accuracy of the logistic regression technique will be closer to 0.98%; with this accuracy, frauds may be easily detected. The fact that LR receives the highest classifier score illustrates how well LR predicts credit card theft.

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Milli, Migraç Enes Furkan, Serkan Aras und İpek Deveci Kocakoç. „Investigating the Effect of Class Balancing Methods on the Performance of Machine Learning Techniques: Credit Risk Application“. İzmir Yönetim Dergisi, 27.06.2024. http://dx.doi.org/10.56203/iyd.1436742.

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Credit risk arises as a result of the failure of the loans given by banks to the customers to fulfill their obligations at the end of the specified term. Technological advances allow the use of machine learning methods in various sectors. These methods aim to facilitate the identification of customers at risk with the system adapted to the creditworthiness processes of banks. For this purpose, in order to make the most appropriate evaluation in the lending process of banks, re-sampling techniques to eliminate the problem of class imbalance encountered in unbalanced data sets were made balanced and their effects on machine learning were investigated. During the implementation phase, German, Australian and HMEQ credit data sets were used. Different machine learning classification methods such as Logistic Regression (LR), K-Narest Neighbor (KNN), Naive Bayes (NB), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Decision Trees (DT), Random Forests (RF), Gradient Boosting Decision Trees (GBDT), Extremely Randomized Trees, Hard and Soft Voting were used to detect risky customers. The problem of class imbalance was balanced with resampling and hybrid techniques such as Random Oversampling (ROS), Random Undersampling (RUS), Balanced Bagging Classifier (BBC), SMOTE-Tomek Links and SMOTE-ENN. In this context, the performances of three different data sets were examined in four different scenarios. As a result of the study, the hybrid method, in which oversampling and undersampling methods are used together for the class balancing problem, showed the best classification performance among machine learning techniques.

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Abdul Salam, Mustafa, Khaled M. Fouad, Doaa L. Elbably und Salah M. Elsayed. „Federated learning model for credit card fraud detection with data balancing techniques“. Neural Computing and Applications, 20.01.2024. http://dx.doi.org/10.1007/s00521-023-09410-2.

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AbstractIn recent years, credit card transaction fraud has resulted in massive losses for both consumers and banks. Subsequently, both cardholders and banks need a strong fraud detection system to reduce cardholder losses. Credit card fraud detection (CCFD) is an important method of fraud prevention. However, there are many challenges in developing an ideal fraud detection system for banks. First off, due to data security and privacy concerns, various banks and other financial institutions are typically not permitted to exchange their transaction datasets. These issues make traditional systems find it difficult to learn and detect fraud depictions. Therefore, this paper proposes federated learning for CCFD over different frameworks (TensorFlow federated, PyTorch). Second, there is a significant imbalance in credit card transactions across all banks, with a small percentage of fraudulent transactions outweighing the majority of valid ones. In order to demonstrate the urgent need for a comprehensive investigation of class imbalance management techniques to develop a powerful model to identify fraudulent transactions, the dataset must be balanced. In order to address the issue of class imbalance, this study also seeks to give a comparative analysis of several individual and hybrid resampling techniques. In several experimental studies, the effectiveness of various resampling techniques in combination with classification approaches has been compared. In this study, it is found that the hybrid resampling methods perform well for machine learning classification models compared to deep learning classification models. The experimental results show that the best accuracy for the Random Forest (RF); Logistic Regression; K-Nearest Neighbors (KNN); Decision Tree (DT), and Gaussian Naive Bayes (NB) classifiers are 99,99%; 94,61%; 99.96%; 99,98%, and 91,47%, respectively. The comparative results show that the RF outperforms with high performance parameters (accuracy, recall, precision and f score) better than NB; RF; DT and KNN. RF achieve the minimum loss values with all resampling techniques, and the results, when utilizing the proposed models on the entire skewed dataset, achieved preferable outcomes to the unbalanced dataset. Furthermore, the PyTorch framework achieves higher prediction accuracy for the federated learning model than the TensorFlow federated framework but with more computational time.

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