Bayesian Classification in Data Mining - Explore the concepts and techniques of Bayesian Classification in Data Mining, including its applications and advantages.
The paper presents an extensive modification of ID3 (Iterative Dichotomiser) algorithm and Naive Bayesian Classification algorithm for data mining. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision ...
These methods can complete data mining task when protecting privacy. This paper gives a new Bayesian-based PPDM method, which is designed for classification. This method is a data perturbation method and is algorithm-independent, which means the perturbed data can be used by normal classification ...
The Bayesian classifier is the classifier with the lowest probability of classification errors among the various classifiers, and the risk is minimal in the case of a given cost [77,93,94]. It is the most basic statistical classification method [95]. The classification principle is to calculate...
kNN is a simple but effective classification method. For a new instance to be classified, k nearest neighbors of the instances are selected, and then the major class of the k neighbors is assigned to the class of the new instance. When the kNN method is employed, the choice of k has a...
classification models such as extreme gradient boosting, gradient boosting decision tree, support vector machine, random forest, and linear discriminant analysis [34]. Similarly, there is a study combining the use of an artificial neural network in the first stage and conditional inference using the ...
Specifically, we first propose a predictive subspace learning method based on factor analysis and define a latent margin loss for classification in the subspace. Then we cast the learning problem into a variational Bayesian framework by exploiting the pseudo-likelihood and data augmentation idea (Zhu ...
Using machine learning terminology, we call (y, X) training data, which are used to fit models; in contrast, the data used only in testing the predictive performance is called test data. For the purposes of feature selection and binary classification, we are interested in modeling the ...
K‐means clustering (KMC) Unsupervised A classification method that divides data into k groups byminimizing within‐group distances to the centroid Reinforcement learning Dynamic programming that trains algorithms using a system of reward and punishment to maximize the performance. Show moreView article ...
In AI, thenaïve Bayesclassifier[58–60]is a general term for aclassification algorithm. The naïve Bayesian method is a classification method based on Bayes’ theorem and characteristic condition-independent hypothesis. Recent studies have often employed Bayesian models to diagnose psychiatric disorder...