With the enormous growth of computer networks and the huge increase in the number of applications that rely on it, network security is gaining increasing importance. Moreover, almost all computer systems suffer from security vulnerabilities which are both technically difficult and economically costly to be solved by the manufacturers. Therefore, the role of Intrusion Detection Systems (IDSs), a...

In Chinese information processing, Naive Bayes is a simple text classification method that is easily implemented. Its core is the realization of the calculating posterior probability algorithm and the effectively reducing dimension for feature words. This paper improved Naive Bayes text classification from the calculating posterior probability and the reducing dimension of feature words of text...

This paper addresses personal E-mail filtering by casting it in the framework of text classification. Modeled as semi-structured documents, Email messages consist of a set of fields with predefined semantics and a number of variable length free-text fields. While most work on classification either concentrates on structured data or free text, the work in this paper deals with both of them. To p...

The emerging multi-core processor architecture has greatly escalated scientific computing, but, at the same time, made parallel programming increasingly complex and challenging. In this paper, the use of the Auto Parallel Classification (APC) model in an Object-Oriented Parallel Model (OOPModel) environment is demonstrated. A designed module provides a traversal and a reduction of the DAG task ...

Traditionally machine learning and data mining research has focused on learning algorithms with high classification or prediction accuracy. This is not always sufficient for some applications that require good algorithm transparency. In this thesis, we apply a random set based high-level knowledge representation language to some well-known data mining algorithms in order to build transparent mo...

Detection of clustered microcalcifications (MCs) in mammograms represents a significant step towards successful detection of breast cancer since their existence is one of the early signs of cancer. In this paper, a new framework that integrates Bayesian classifier and a pattern synthesizing scheme for detecting microcalcification clusters is proposed. This proposed work extracts textural, spect...

This paper illustrates a new criterion for evaluating neural networks stability compared to the Bayesian classifier. The stability comparison is performed by the error rate probability densities estimation using the modified semi-bounded Plug-in algorithm. We attempt, in this work, to demonstrate that the Bayesian approach for neural networks improves the performance and stability degree of the...