We propose a pool-based non-parametric active learning algorithm for general metric spaces, called MArgin Regularized Metric Active Nearest Neighbor (MARMANN), which outputs a nearest-neighbor classifier. We give prediction error guarantees that depend on the noisy-margin properties of the input sample, and are competitive with those obtained by previously proposed passive learners. We prove th...

A distance based classification is one of the popular methods for classifying instances using a point-to-point distance based on the nearest neighbour or k-NEAREST NEIGHBOUR (k-NN). The representation of distance measure can be one of the various measures available (e.g. Euclidean distance, Manhattan distance, Mahalanobis distance or other specific distance measures). In this paper, we propose ...

Superconducting phase diagram of the extended Hubbard model supplemented with interaction and hopping terms exceeding nearest neighbour distance in range is analysed systematically at different band-filling and temperature values in a mean-field approximation. The obtained results clearly underline the importance of next-nearest neighbour terms in developing the main superconducting properties ...

This paper presents new approach Hesitant Fuzzy K-nearest neighbour (HFK-nn) based document classification and numerical results analysis. The proposed classification Hesitant Fuzzy K-nearest neighbour (HFKnn) approach is based on hesitant Fuzzy distance. In this paper we have used hesitant Fuzzy distance calculations for document classification results. The following steps are used for classif...

Drug repositioning helps identify new indications for marketed drugs and clinical candidates. In this study, we proposed an integrative computational framework to predict novel drug indications for both approved drugs and clinical molecules by integrating chemical, biological and phenotypic data sources. We defined different similarity measures for each of these data sources and utilized a weig...

This paper explores a nearest neighbour pattern recognition method for time-series forecasting. A nearest neighbour method (FNNM) based on fuzzy membership values is developed. The main aim of the forecasting algorithm is to make single point forecasts into the future on the basis of past nearest neighbours. The nearest neighbours are selected using a membership threshold value. The results inc...

It is known that an ensemble of classifiers can outperform a single best classifier if classifiers in the ensemble are sufficiently diverse (i.e., their errors are as much uncorrelated as possible) and accurate. We study ensembles of nearest neighbours for cancer classification based on gene expression data. Such ensembles have been rarely used, because the traditional ensemble methods such as ...

In this paper, we present an evaluation of nearest neighbour searching using the Area Code tree. The Area Code tree is a trie-type structure that organizes area code representations of each point of interest (POI) in a data set. This data structure provides a fast method for locating an actual or approximate nearest neighbour POI for a query point. We first summarize the area code generation, i...

In this paper, we empirically examine the use of a range of Minkowski norms for the clustering of real world data. We also investigate whether normalisation of the data prior to clustering affects the quality of the result. In a nearest neighbour search on raw real world data sets, fractional norms outperform the Euclidean and higher-order norms. However, when the data are normalised, the resul...

Most of the existing phishing detection techniques are weak against domain name system (DNS)-poisoning-based phishing attacks. Proposed is a highly effective method for detecting such attacks: the network performance characteristics of websites are used for classification. To demonstrate how useful the approach is, the performance of four classification algorithms are explored: linear discrimin...