We suggest a new framework for classification rule mining in quantitative data sets founded on Bayes theory – without univariate preprocessing of attributes. We introduce a space of rule models and a prior distribution defined on this model space. As a result, we obtain the definition of a parameter-free criterion for classification rules. We show that the new criterion identifies interesting c...

W develop, estimate, and test a response model of order timing and order volume decisions of catalog customers and derive a Bayes rule for optimal mailing strategies. The model integrates the when and how much components of the response; incorporates the mailing decision of the firm; and uses a Bayesian framework to determine the optimal mailing rule for each catalog customer. The Bayes rule we...

We study a model of non-Bayesian updating, based on the Hypothesis Testing model of Ortoleva (2012), for ambiguity averse agents. Agents ranks acts following the MaxMin Expected Utility model of Gilboa and Schmeidler (1989) and when they receive new information they update their set of priors as follows: If the information is such that all priors in the original set of priors assign to it a pro...

Let X be a random variable taking values in a compact Riemannian manifold without boundary, and let Y be a discrete random variable valued in {0; 1} which represents a classification label. We introduce a kernel rule for classification on the manifold based on n independent copies of (X, Y ). Under mild assumptions on the bandwidth sequence, it is shown that this kernel rule is consistent in th...

Naive Credal Classifier, which is an imprecise-probability counterpart of Naive Bayes, is rigorously extended to a very general and flexible treatment of incomplete data, yielding a new classifier called Naive Credal Classifier 2 (NCC2). The new classifier delivers classifications that are robust to the presence of small sample sizes and missing values. In particular, some empirical evaluations...

Nowadays, new methods are required to take advantage of the rich and extensive gold mine of data given the vast content of data particularly created by educational systems. Data mining algorithms have been used in educational systems especially e-learning systems due to the broad usage of these systems. Providing a model to predict final student results in educational course is a reason for usi...

Abstract−This study intends to predict the harvest period in farming at Regional Technical Implementation Unit of Gabe Hutaraja Various Crops. The method used this is Naive Bayes Classifier. data are time series from 2015 2017. results predictions rice, peanut, and corn crops future.
 Classifier a simple probabilistic classification process based on application Bayes' Theorem (Bayes rule) ...