Given a query location Q in multi-dimensional space, the classical KNN problem is to return the K spatially closest answers in the database with respect to Q. The KNDN (K-Nearest Diverse Neighbor) problem is a semantic extension where the objective is to return the spatially closest result set such that each answer is sufficiently different, or diverse, from the rest of the answers. We review h...

We applied a multi-class k-nearest-neighbor based text classiication algorithm to the adap-tive and batch ltering problems in the TREC-9 ltering track. While our systems performed well in the batch ltering tasks, they did not perform as well in the adaptive ltering tasks, in part because we did not have an adequate mechanism for taking advantage of the relevance feedback information provided by...

18 Impact of Facebook Ads for Sexual Health Promotion Via an Educational Web App: A Case Study; Elia Gabarron, Norwegian Centre for E-health Research, University Hospital of North Norway, Tromsø, Norway & Department of Clinical Medicine, Faculty of Health Sciences, The Arctic University of Norway, Tromsø, Norway Luis Fernandez Luque, Salumedia Tecnologias, Dos Hermanas, Spain & NORUT, Northern ...

The classical k nearest neighbor (k-nn) classification assumes that a fixed global metric is defined and searching for nearest neighbors is always based on this global metric. In the paper we present a model with local induction of a metric. Any test object induces a local metric from the neighborhood of this object and selects k nearest neighbors according to this locally induced metric. To in...

This paper addresses a task of automatically identifying abnormal chat users where training data is given as a collection of chat messages from both abnormal and normal users. We employ a k-NN classification based on an IR technique. A document is constructed in per-conversation for each user by concatenating his/her messages in a conversation. A query is constructed for a new user in the same ...

A nearest-neighbor chain (NNC) based approach is proposed in this paper to develop a skew estimation method with a high accuracy and with language-independent capability. Size restriction is introduced to the detection of nearest-neighbors (NN). Then NNCs are extracted from the adjacent NN pairs, in which the slopes of the NNCs with a largest possible number of components are computed to give t...

In the paper a new measure of distance between events/observations in the pattern space is proposed and experimentally evaluated with the use of k-NN classifier in the context of binary classification problems. The application of the proposed approach visibly improves the results compared to the case of training without postulated enhancements in terms of speed and accuracy. Numerical results a...

A continuing challenge for software designers is to develop efficient and cost-effective software implementations. Many see software reuse as a potential solution; however, the cost of reuse tends to outweigh the potential benefits. The costs of software reuse include establishing and maintaining a library of reusable components, searching for applicable components to be reused in a design, as ...

We provide theoretical and algorithmic tools for nding new features which enable better classiication of new cases. Such features are proposed to be searched for as linear combinations of continuously valued conditions. Regardless of the choice of classiication algorithm itself, such an approach provides the compression of information concerning dependencies between conditional and decision fea...