Towards Detecting Deception using K-Nearest Neighbour Model

k-Nearest Neighbour Classifiers

Perhaps the most straightforward classifier in the arsenal or machine learning techniques is the Nearest Neighbour Classifier – classification is achieved by identifying the nearest neighbours to a query example and using those neighbours to determine the class of the query. This approach to classification is of particular importance today because issues of poor run-time performance is not such...

Efficient model selection for probabilistic K nearest neighbour classification

ProbabilisticK-nearest neighbour (PKNN) classification has been introduced to improve the performance of the original K-nearest neighbour (KNN) classification algorithm by explicitly modelling uncertainty in the classification of each feature vector. However, an issue common to both KNN and PKNN is to select the optimal number of neighbours, K. The contribution of this paper is to incorporate t...

A Critical Constant for the k Nearest-Neighbour Model

Let P be a Poisson process of intensity one in a square Sn of area n. For a fixed integer k, join every point of P to its k nearest neighbours, creating an undirected random geometric graph Gn,k. We prove that there exists a critical constant ccrit such that for c < ccrit, Gn,⌊c logn⌋ is disconnected with probability tending to 1 as n → ∞, and for c > ccrit, Gn,⌊c logn⌋ is connected with probab...

Localization using a Region-Based k-Nearest Neighbour Search

This paper explores a method of performing localization on local vision mobile robots. It describes a method of processing images, extracting regions, and comparing those regions against a database of preprocessed images. Localization is achieved using the k-nearest neighbour algorithm as the basis for approximating the current position. Initial results are provided that show the potential of t...

Coevolving Functions in Genetic Programming: Classification using K-nearest-neighbour