Bayesian Computation Statistics
Rashin Nimaei; Farzad Eskandari
Abstract
The recent advancements in technology have faced an increase in the growth rate of data.According to the amount of data generated, ensuring effective analysis using traditional approaches becomes very complicated.One of the methods of managing and analyzing big data ...
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The recent advancements in technology have faced an increase in the growth rate of data.According to the amount of data generated, ensuring effective analysis using traditional approaches becomes very complicated.One of the methods of managing and analyzing big data is classification.%One of the data mining methods used commonly and effectively to classify big data is the MapReduceIn this paper, the feature weighting technique to improve Bayesian classification algorithms for big data is developed based on Correlative Naive Bayes classifier and MapReduce Model.%Classification models include Naive Bayes classifier, correlated Naive Bayes and correlated Naive Bayes with feature weighting.Correlated Naive Bayes classification is a generalization of the Naive Bayes classification model by considering the dependence between features.%This paper uses the feature weighting technique and Laplace calibration to improve the correlated Naive Bayes classification.The performance of all described methods are evaluated by considering accuracy, sensitivity and specificity, accuracy, sensitivity and specificity metrics.
Bayesian Network
Vahid Rezaei Tabar; Mohaddeseh Safakish
Abstract
In the modern era, detecting credit card fraud has become a crucial concern from both financial and security standpoints. Given the rarity of fraudulent activities, the issue is reframed as a binary classification challenge, tackling the complexities of imbalanced datasets. To address this, authors advocate ...
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In the modern era, detecting credit card fraud has become a crucial concern from both financial and security standpoints. Given the rarity of fraudulent activities, the issue is reframed as a binary classification challenge, tackling the complexities of imbalanced datasets. To address this, authors advocate using Bayesian networks due to their theoretical robustness and capacity to model intricate scenarios while maintaining interpretability in the context of class skewed distributions. A pivotal component of this meta learning framework is the cost matrix, leading authors to explore various techniques for its calculation. By employing our meta-learning framework with data from Iran’s banking system, the authors demonstrate a method for determining the cost matrix. Subsequently, develop the corresponding Cost Augmented Bayesian Network Classifiers, called CABNCs. The outcomes highlight the potential of CATAN to diminish financial loss and the effectiveness of CAGHC-K2 in predicting labels for forthcoming transactions in the context of class imbalance.
Mathematical Computing
Zahra Behdani; Majid Darehmiraki
Abstract
The Fuzzy K-Nearest Neighbour (FKNN) method is a classification approach that integrates fuzzy theories with the K-Nearest Neighbour classifier. The algorithm computes the degree of membership for a given dataset within each class and then chooses the class with the highest degree of membership as the ...
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The Fuzzy K-Nearest Neighbour (FKNN) method is a classification approach that integrates fuzzy theories with the K-Nearest Neighbour classifier. The algorithm computes the degree of membership for a given dataset within each class and then chooses the class with the highest degree of membership as the assigned classification outcome. This algorithm has several applications in regression problems. When the mathematical model of the data is not known, this method can be used to estimate and approximate the value of the response variable. This paper introduces a method, which incorporates a parameter distance measure to empower decision makers to make precise selections across several levels. Furthermore, we provide an analysis of the algorithm's strengths and shortcomings, as well as a comprehensive explanation of the distinctions between the closest neighbour approach in tasks of classification and regression. Finally, to further elucidate the principles, we present a range of examples that demonstrate the application of closest neighbour algorithms in the classification and regression of fuzzy numbers.
