In this paper proposed a hybrid model for feature selection and intrusion detection. Feature selection is important issue in intrusion detection. The selection of feature in attack attribute and normal traffic attribute is challenging task. The selection of known and unknown attack is also faced a problem of classification. PCNN is dynamic network used for the process of feature selection in cl...

This paper addresses the classification problem with imperfect data. More precisely, it extends standard decision trees to handle uncertainty in both building and classification procedures. Uncertainty here is represented by means of possibility distributions. The first part investigates the issue of building decision trees from data with uncertain class values by developing a non-specificity b...

In Dempster–Shafer evidence theory (DST) based classifier design, Dempster’s combination (DC) rule is commonly used as a multi-attribute classifier to combine evidence collected from different attributes. The main aim of this paper is to present a classification method using a novel combination rule i.e., the evidence reasoning (ER) rule. As an improvement of the DC rule, the newly proposed ER ...

We introduce a method for improving facial attribute predictions using other attributes. In the domain of face recognition and verification, attributes are high-level descriptions of face images. Attributes are very useful for identification as well as image search as they provide easily understandable descriptions of faces, rather than most other image descriptors (i.e. HOG, LBP, and SIFT). A ...

Weighted association rule mining reflects semantic significance of item by considering its weight. Classification constructs the classifier and predicts the new data instance. This paper proposes compact weighted class association rule mining method, which applies weighted association rule mining in the classification and constructs an efficient weighted associative classifier. This proposed as...