Cognitive radio networks operation relies on the capability to gather information about the surrounding environment, as regards both internal network status (presence and capabilities of other devices belonging to the same network) and external systems potentially coexisting with the cognitive network. In particular, retrieving information at the local level is a function required to all wirele...

Finding Nearest Neighbors efficiently is crucial to the design of any nearest neighbor classifier. This paper shows how Layered Range Trees could be used for efficient nearest neighbor classification. The presented algorithm is simple and finds the nearest neighbor in a logarithmic order. It performs d log n + k distance measures to find the nearest neighbor, where k is a constant that is much ...

In many applications, different kinds of moments have been utilized to classify images and object shapes. Moments are important features used in recognition of different types of images. In this paper, three kinds of moments: Geometrical, Zernike and Legendre Moments have been evaluated for classifying 3D object images using Nearest Neighbor classifier. Experiments are conducted using ETH-80 da...

A class-dependent weighted (CDW) dissimilarity measure in vector spaces is proposed to improve the performance of the nearest neighbor classifier. In order to optimize the required weights, an approach based on Fractional Programming is presented. Experiments with several standard benchmark data sets show the effectiveness of the proposed technique.

Local coordinates based on the Voronoi diagram are used in natural neighbour interpolation to quantify the “neighbourliness” of data sites. In an earlier paper, we have extended the natural neighbour or stolen area interpolation technique from ordinary Voronoi diagrams to Voronoi diagrams for sets of points and line segments, by providing direct vectorial formulas for the first order and second...

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...

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...

A new fuzzy-rough nearest neighbour (FRNN) classification algorithm is presented in this paper, as an alternative to Sarkar’s fuzzyrough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the nearest neighbours to construct lower and upper approximations of decision classes, and classifies test instances based on their membership to these approximations. In the exp...

The neighbourhood polynomial G , is generating function for the number of faces of each cardinality in the neighbourhood complex of a graph. In other word $N(G,x)=sum_{Uin N(G)} x^{|U|}$, where N(G) is neighbourhood complex of a graph, whose vertices are the vertices of the graph and faces are subsets of vertices that have a common neighbour. In this paper we compute this polynomial for some na...