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...

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...

In this paper we propose and explore the k-Nearest Neighbour UCB algorithm for multiarmed bandits with covariates. We focus on a setting where the covariates are supported on a metric space of low intrinsic dimension, such as a manifold embedded within a high dimensional ambient feature space. The algorithm is conceptually simple and straightforward to implement. The k-Nearest Neighbour UCB alg...

We examine the use of recurrence networks in studying non-linear deterministic dynamical systems. Specifically, we focus on the case of k-nearest neighbour networks, which have already been shown to contain meaningful (and more importantly, easily accessible) information about dynamics. Superfamily phenomena have previously been identified, although a complete explanation for its appearance was...

The nearest neighbour (NN) and k-nearest neighbour (k-NN) classification rules have been widely used in Pattern Recognition due to its simplicity and good behaviour. Exhaustive nearest neighbour search may become unpractical when facing large training sets, high dimensional data or expensive dissimilarity measures (distances). During the last years a lot of fast NN search algorithms have been d...

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...