A classifier is called consistent with respect to a given set of classlabeled points if it correctly classifies the set. We consider classifiers defined by unions of local separators and propose algorithms for consistent classifier reduction. The expected complexities of the proposed algorithms are derived along with the expected classifier sizes. In particular, the proposed approach yields a c...

A new approach, based on the k-Nearest Neighbor (kNN) classifier, is used to classify program behavior as normal or intrusive. Program behavior, in turn, is represented by frequencies of system calls. Each system call is treated as a word and the collection of system calls over each program execution as a document. These documents are then classified using kNN classifier, a popular method in te...

In this paper, we propose an automatic image annotation approach for region labeling that takes advantage of both context and semantics present in segmented images. The proposed approach is based on multi-class K-nearest neighbor, k-means and particle swarm optimization (PSO) algorithms for feature weighting, in conjunction with normalized cuts-based image segmentation technique. This hybrid ap...

The k-Nearest Neighbor (k-NN) classification algorithm is one of the most widely-used lazy classifiers because of its simplicity and ease of implementation. It is considered to be an effective classifier and has many applications. However, its major drawback is that when sequential search is used to find the neighbors, it involves high computational cost. Speeding-up k-NN search is still an act...

Data quality was placed as a major reason for the low utility of patient safety event reporting systems. A pressing need in improving data quality has advanced recent research focus in data entry associated with human factors. The debate on structured data entry or unstructured data entry reveals not only a trade-off problem among data accuracy, completeness, and timeliness, but also a technica...

For the last few years, a extensive research has been going on query processing of relation data and more practical and theoretical solution have been suggested to query processing under different scenarios. Now days cloud computing technology is increasing rapidly, so users now have the chance to store their data in remote location. However, different privacy issues are raised on cloud computi...

A nonparametric approach is proposed to combine several individual classifiers in order construct an asymptotically more accurate classification rule the sense that its misclassification erro...

This paper compares the efficacy of two anomaly detection classifiers with respect to the classification of processes as either intrusive or non-intrusive. To the task of process classification, both classifiers treat processes as system call sequences, encode those system call sequences as text documents, and apply the k-nearest neighbor text categorization method to classify the processes. In...

In recent years, many nearest neighbor algorithms based on fuzzy sets theory have been developed. These methods form a field, known as fuzzy nearest neighbor classification, which is the source of many proposals for the enhancement of the k nearest neighbor classifier. Fuzzy sets theory and several extensions, including fuzzy rough sets, intuitionistic fuzzy sets, type-2 fuzzy sets and possibil...