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Relevant bibliographies by topics / Naive credal classifier

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Academic literature on the topic 'Naive credal classifier'

Author: Grafiati

Published: 30 November 2024

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Journal articles on the topic "Naive credal classifier"

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 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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4

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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8

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 trees built with the best possible split criterion obtained in (1).

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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 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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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 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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