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...

This article examines the performance of two one-class classification-based control charts through a real industrial application. These two control charts are the kernel distance–based control chart, known as the K chart, and the k-nearest neighbour data description-based control chart, referred to as the KNN chart. We studied the effectiveness of both charts in detecting out-ofcontrol observat...

Data reduction is a common pre-processing step for k-nearest neighbor classification (kNN). The existing prototype selection methods implement different criteria for selecting relevant points to use in classification, which constitutes a selection bias. This study examines the nature of the instance selection bias in intrinsically high-dimensional data. In high-dimensional feature spaces, hubs ...

In this paper, we present an experimental current-mode Kohonen neural network (KNN) implemented in a CMOS 0.18 m process. The network contains four output neurons. Each neuron has three analog weights related to three inputs. The presented KNN has been realized using building blocks proposed earlier by the authors, such as binary tree current-mode winner takes all (WTA) circuit, Euclidean dista...

Because of it is applicability in various field, multi-instance learning or multi-instance problem becoming more popular in machine learning research field. Different from supervised learning, multi-instance learning related to the problem of classifying an unknown bag into positive or negative label such that labels of instances of bags are ambiguous. This paper uses and study three different ...

Efficient data indexing and exact k-nearest-neighbor (kNN) retrieval are still challenging tasks in high-dimensional spaces. This work highlights the difficulties of indexing in high-dimensional and tightly-clustered dataspaces by exploring several important tunable parameters for optimizing kNN query performance using the iDistance and iDStar algorithms. We experiment on real and synthetic dat...

Density-based clustering algorithms for multivariate data often have difficulties with high-dimensional data and clusters of very different densities.A new density-based clustering algorithm, called KNNCLUST, is presented in this paper that is able to tackle these situations. It is based on the combination of nonparametric k-nearest-neighbor (KNN) and kernel (KNN-kernel) density estimation. The...

The learning capacity and the classification ability for normal beats and premature ventricular contractions clustering by four classification methods were compared: neural networks (NN), K-th nearest neighbour rule (Knn), discriminant analysis (DA) and fuzzy logic (FL). Twenty-six morphology feature parameters, which include information of amplitude, area, specific interval durations and measu...

The aim of automated electrocardiogram (ECG) delineation system is the reliable detection of fundamental ECG components and from these fundamental measurements, the parameters of diagnostic significance, namely, P-duration, PR-interval, QRS-duration, QTinterval, are to be identified and extracted. In this work, two supervised machine learning algorithms, K-Nearest neighbour (KNN) and Support Ve...