Corporate directors are influenced by overconfidence, which is one of the personality traits of individuals; it may take irrational decisions that will have a significant impact on the company's performance in the long run. The purpose of this paper is to validate and compare the Naive Bayesian Classification algorithm and probit regression in the prediction of Management's overconfident at pre...

We introduce a conservative fuzzy logic extension of the Naive Bayesian classification algorithm. The extension generalizes the algorithm such that the examples are described by a fuzzy set of attributes, instead of a classical set. Thus, an example possesses each attribute to a degree in [0, 1]. We present a new classification algorithm usable with fuzzy sets that is (a) fast, (b) is able to w...

In the data mining field, classification is a very crucial technology, and the Bayesian classifier has been one of the hotspots in classification research area. However, assumptions of Naive Bayesian and Tree Augmented Naive Bayesian (TAN) are unfair to attribute relations. Therefore, this paper proposes a new algorithm named Fuzzy Naive Bayesian (FNB) using neural network with weighted members...

This paper analyzed spam filtering technology, carried out a detailed study of Naive Bayes algorithm, and proposed the improved Naive Bayesian mail filtering technology. Improvement can be seen in text selection as well as feature extraction. The general Bayesian text classification algorithm mostly takes information gain and cross-entropy algorithm in feature selection. Through the principle o...

This article investigates boosting naive Bayesian classification. It first shows that boosting does not improve the accuracy of the naive Bayesian classifier as much as we expected in a set of natural domains. By analyzing the reason for boosting’s weakness, we propose to introduce tree structures into naive Bayesian classification to improve the performance of boosting when working with naive ...

Different probabilistic models for classification and prediction problems are anlyzed in this article studying their behaviour and capability in data classification. To show the capability of Bayesian Networks to deal with classification problems four types of Bayesian Networks are introduced, a General Bayesian Network, the Naive Bayes, a Bayesian Network Augmented Naive Bayes and the Tree Aug...

This paper improves the naïve bayesian classification algorithm , combining with the rough set theory we can get a naive bayesian classifier algorithm based on the rough set. We implement this algorithm on a cloud platform using map-reduce programming mode and get a excellent result. A recall rate of 76.4 was achieved when classifying Tibetan Web pages .

The Semi-Naive Bayesian network (SNB) classifier, a probabilistic model with an assumption of conditional independence among the combined attributes, shows a good performance in classification tasks. However, the traditional SNBs can only combine two attributes into a combined attribute. This inflexibility together with its strong independency assumption may generate inaccurate distributions fo...