In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements betw...

The paper proposes a hybrid feature selection approach based on Rough sets and Bayesian network classifiers. In the approach, the classification result of a Bayesian network is used as the criterion for the optimal feature subset selection. The Bayesian network classifier used in the paper is a kind of naive Bayesian classifier. It is employed to implement classification by learning the samples...

This paper discusses issues related to Bayesian network model learning for unbalanced binary classification tasks. In general, the primary focus of current research on Bayesian network learning systems (e.g., K2 and its variants) is on the creation of the Bayesian network structure that fits the database best. It turns out that when applied with a specific purpose in mind, such as classificatio...

Automatic speech recognition has generally been treated as a problem of Bayesian classification. This is suboptimal when the distributions of the training data do not match those of the test data to be recognized. In this paper we propose an alternate analogous classification paradigm, in which classes are modeled by thermodynamic systems, and classification is performed through a minimum energ...

Hilbig and Moshagen (Psychonomic Bulletin & Review, 21, 1431-1443, 2014) recently developed a method for making inferences about the decision processes people use in multi-attribute forced choice tasks. Their paper makes a number of worthwhile theoretical and methodological contributions. Theoretically, they provide an insightful psychological motivation for a probabilistic extension of the wid...

This document discusses Bayesian classification in the context of supervised learning. Supervised learning is defined. An approach is described in which feature likelihooods are estimated from data, and then classification is done by computing class posteriors given features using Bayes rule. Estimating of feature likelihoods, independence of features, quantization of features, and information ...

Large numbers of overlapping classes are found to be organized in hierarchies in many domains. In multi-label classification over such a hierarchy, members of a class must also belong to all of its parents. Training an independent classifier for each class is a common approach, but this may yield labels for a given example that collectively violate this constraint. We propose a principled metho...

We outline the principles of Bayesian classification using discrete templates. We show that this form of nonparametric modelling is semi-supervised learning and embodies multi-level hierarchical modelling to infer all model parameters from the data. CU8 GAIA-C8-TN-MPIA-CBJ-061 Document History Issue Revision Date Author Comment 1 1 2011-08-29 CBJ Added softmax implementation 1