We propose combining advanced statistical approaches with data mining techniques to build classifiers to enhance decision-making models for the job assignment problem. Adaptive Generalized Estimation Equation (AGEE) approaches with Gibbs sampling under Bayesian framework and adaptive Bayes classifiers based on the estimations of AGEE models which uses modified Naive Bayes algorithm are proposed...

Developing two machine learning classifiers with higher accuracy for classifying income classes people earning less and a salary scale between 50,000. Decision Tree Algorithm (DTA) Naive Bayes (NBA) are the classifier mechanisms employed. On dataset of 32516 records, methods were implemented tested. Implemented each algorithm through programs performed ten rounds on both to determine distinct s...

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...

Data mining is a computational approach aiming to discover hidden and valuable information in large datasets. It has gained importance recently in the wide area of computational among which many in the domain of Business Informatics. This paper focuses on applications of data mining in Customer Relationship Management (CRM). The core of our application is a classifier based on the naive Bayesia...

In this paper, we examine previous work on the naive Bayesian classifier and review its limitations, which include a sensitivity to correlated features. We respond to this problem by embedding the naive Bayesian induction scheme within an algorithm that carries out a greedy search through the space of features. We hypothesize that this approach will improve asymptotic accuracy in domains that i...

Conventional Bayesian networks often require discretization of continuous variables prior to learning. It is important to investigate Bayesian networks allowing mixed-mode data, in order to better represent data distributions as well as to avoid the overfitting problem. However, this attempt imposes potential restrictions to a network construction algorithm, since certain dependency has not bee...

Machine learning has been an effective support system in medical diagnosis which involve large amount of data. Analyzing such data consumes more time in terms of execution and resources. All data features do not support for the end results. Hence it is very important to identify the features that contribute more in identifying the diseases. Those with less contribution can be eliminated. The ne...

Naive Bayes has been widely used in data mining as a simple and effective classification algorithm. Since its conditional independence assumption is rarely true, numerous algorithms have been proposed to improve naive Bayes, among which tree augmented naive Bayes (TAN) [3] achieves a significant improvement in term of classification accuracy, while maintaining efficiency and model simplicity. I...