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Journal articles on the topic 'Naive credal classifier'

Author: Grafiati

Published: 30 November 2024

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1

Zaffalon, Marco. "The naive credal classifier." Journal of Statistical Planning and Inference 105, no. 1 (2002): 5–21. http://dx.doi.org/10.1016/s0378-3758(01)00201-4.

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2

Antonucci, Alessandro, and 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, and ANDRÉS R. MASEGOSA. "IMPRECISE CLASSIFICATION WITH CREDAL DECISION TREES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, no. 05 (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 o

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Moral-García, Serafín, Javier G. Castellano, Carlos J. Mantas, and Joaquín Abellán. "Using extreme prior probabilities on the Naive Credal Classifier." Knowledge-Based Systems 237 (February 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, no. 6 (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, and Y. M. Zhang. "A novel approach to transformer fault diagnosis using IDM and naive credal classifier." International Journal of Electrical Power & Energy Systems 105 (February 2019): 846–55. http://dx.doi.org/10.1016/j.ijepes.2018.09.029.

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7

Zaffalon, Marco, Keith Wesnes, and Orlando Petrini. "Reliable diagnoses of dementia by the naive credal classifier inferred from incomplete cognitive data." Artificial Intelligence in Medicine 29, no. 1-2 (2003): 61–79. http://dx.doi.org/10.1016/s0933-3657(03)00046-0.

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ABELLÁN, JOAQUÍN, and 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, no. 06 (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 tr

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9

Chen, Yihong. "Credit card customers churn prediction by nine classifiers." Applied and Computational Engineering 48, no. 1 (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

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10

Takawira, Oliver, and John W. Muteba Mwamba. "DETERMINANTS OF SOVEREIGN CREDIT RATINGS: AN APPLICATION OF THE NAÏVE BAYES CLASSIFIER." Eurasian Journal of Economics and Finance 8, no. 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 ra

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11

Yang, Zhen. "Utilization of Quantization Method on Credit Risk Assessment." Applied Mechanics and Materials 472 (January 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 a

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12

., 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, no. 04 (2017): 43–44. http://dx.doi.org/10.15623/ijret.2017.0604010.

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13

Farrales, Victorino, Jonnifer Mandigma, Casielyn Capistrano, Severino Bedis, Jr., and Aleta Fabregas. "Credit Assessment and Recommendation System (CARS) using Naive Bayesian Algorithm." Technologique: A Global Journal on Technological Developments and Scientific Innovations 2, no. 1 (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

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14

Khandale, Shreyas, Prathamesh Patil, and Rohan Patil. "Predicting Credit Card Defaults with Machine Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 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

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Antika, Dwi Putri, Mohamat Fatekurohman, and I. Made Tirta. "Banking Credit Risk Analysis with Naive Bayes Approach and Cox Proportional Hazard." International Journal of Advanced Engineering Research and Science 9, no. 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 classi

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16

Prasetya, Ichwanul Kahfi, Devi Putri Isnawarty, Abdullah Fahmi, Salman Alfarizi Pradana Andikaputra, and Wibawati Wibawati. "Comparing the Performance of Multivariate Hotelling’s T2 Control Chart and Naive Bayes Classifier for Credit Card Fraud Detection." Inferensi 7, no. 1 (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, no. 5 (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 orde

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18

Qasem, Mais Haj, and Loai Nemer. "Extreme Learning Machine for Credit Risk Analysis." Journal of Intelligent Systems 29, no. 1 (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-l

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19

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, no. 1 (2018): 1. http://dx.doi.org/10.11648/j.ajdmkd.20180301.11.

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20

Damanik, Joel Rayapoh, Rahmat Fauzi, and 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, no. 4 (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 Lear

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21

Wu, Xian, and Huan Liu. "Application of Big Data Unbalanced Classification Algorithm in Credit Risk Analysis of Insurance Companies." Journal of Mathematics 2022 (March 25, 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 I

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22

Wu, Jindi. "Comparison of machine learning algorithms for credit card fraud transaction prediction." Applied and Computational Engineering 6, no. 1 (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,

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23

Shimu Khatun, Mst, Bhuiyan Rabiul Alam, Md Taslim, and Md Alam Hossain. "Handling Class Imbalance in Credit Card Fraud Using Various Sampling Techniques." American Journal of Multidisciplinary Research and Innovation 1, no. 4 (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 und

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24

Kumain, Kiran. "Analysis of Fraud Detection on Credit Cards using Data Mining Techniques." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 11, no. 1 (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. Bot

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25

Vikash Chander Maheshwari, Nurul Aida Osman, and 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, no. 1 (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 alarm

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26

"Swindling Shonky Anatomization of Credit Card Transactions using Machine Learning." International Journal of Recent Technology and Engineering 8, no. 4 (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

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27

"Ensemble Classification Method for Credit Card Fraud Detection." International Journal of Recent Technology and Engineering 8, no. 3 (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 frau

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28

Tanza, Alifia, and 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, August 29, 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 ac

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29

Ileberi, Emmanuel, Yanxia Sun, and Zenghui Wang. "A machine learning based credit card fraud detection using the GA algorithm for feature selection." Journal of Big Data 9, no. 1 (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 fol

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30

Tripathy, Nrusingha, Subrat Kumar Nayak, Julius Femi Godslove, Ibanga Kpereobong Friday, and 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 u

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31

Milli, Migraç Enes Furkan, Serkan Aras, and İpek Deveci Kocakoç. "Investigating the Effect of Class Balancing Methods on the Performance of Machine Learning Techniques: Credit Risk Application." İzmir Yönetim Dergisi, June 27, 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

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32

Abdul Salam, Mustafa, Khaled M. Fouad, Doaa L. Elbably, and Salah M. Elsayed. "Federated learning model for credit card fraud detection with data balancing techniques." Neural Computing and Applications, January 20, 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

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