Bayesian network are powerful probabilistic graphical models for modelling uncertainty. Among others, classification represents an important application: some of the most used classifiers are based on Bayesian networks. Bayesian networks are precise models: exact numeric values should be provided for quantification. This requirement is sometimes too narrow. Sets instead of single distributions ...

We employ random geometric digraphs to construct semi-parametric classifiers. These data-random digraphs are from parametrized random digraph families called proximity catch digraphs (PCDs). A related geometric digraph family, class cover catch digraph (CCCD), has been used to solve the class cover problem by using its approximate minimum dominating set. CCCDs showed relatively good performance...

in this paper, we propose an approach for automatic generation of novel intrusion signatures. this approach can be used in the signature-based network intrusion detection systems (nidss) and for the automation of the process of intrusion detection in these systems. in the proposed approach, first, by using several one-class classifiers, the profile of the normal network traffic is established. ...

One-Class Classification (OCC) has been prime concern for researchers and effectively employed in various disciplines. But, traditional methods based one-class classifiers are very time consuming due to its iterative process and various parameters tuning. In this paper, we present six OCC methods and their thirteen variants based on extreme learning machine (ELM) and Online Sequential ELM (OSEL...

We introduce the family of multi-dimensional Bayesian network classifiers. These classifiers include one or more class variables and multiple feature variables, which need not be modelled as being dependent on every class variable. Our family of multi-dimensional classifiers includes as special cases the well-known naive Bayesian and tree-augmented classifiers, yet offers better modelling capab...

The evaluation and use of classifiers is based on the idea that a classifier is defined as a complete function from instances to classes. Even when probabilistic classifiers are used, these are ultimately converted into categorical classifiers that must choose one class (with more or less confidence) from a set of classes. Evaluation metrics such as accuracy/error, global cost, precision, recal...