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Letteratura scientifica selezionata sul tema "Naive credal classifier"

Autore: Grafiati

Pubblicato: 30 novembre 2024

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Articoli di riviste sul tema "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 o

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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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Abstract (sommario):

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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Abstract (sommario):

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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Abstract (sommario):

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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