Editing allows the selection of a representative subset of prototypes among the training sample to improve the performance of a classi cation task. The Wilson's editing algorithm was the rst proposal and then a great variety of new editing techniques have been proposed based on it. This algorithm consists on the elimination of prototypes in the training set that are misclassi ed using the k-NN ...

Voting rules relying on k-nearest neighbors (k-NN) are an effective tool in countless many machine learning techniques. Thanks to its simplicity, k-NN classification is very attractive to practitioners, as it enables very good performances in several practical applications. However, it suffers from various drawbacks, like sensitivity to “noisy” instances and poor generalization properties when ...

In this paper we propose an online approximate k-nn graph building algorithm, which is able to quickly update a k-nn graph using a flow of data points. One very important step of the algorithm consists in using the current distributed graph to search for the neighbors of a new node. Hence we also propose a distributed partitioning method based on balanced k-medoids clustering, that we use to op...

In this paper a comparative analysis of K-Principle Component Analysis (K-PCA) and K-Nearest Neighbor (K-NN) classifier is done for age invariant face recognition using Indian Face Age Database (IFAD). IFAD is a real time and wild in face database which can be used for face recognition at different variation parameters. These variations can be pose, illumination, occulation, and age. In this pa...

K-Nearest Neighbor (k-NN) queries are used in GIS and CAD/CAM applications to find the k spatial objects closest to some given query points. Most previous k-NN research has assumed that the spatial databases to be queried are local, and that the query processing algorithms have direct access to their spatial indices, e.g. R-trees. Clearly, this assumption does not hold when k-NN queries are dir...

In the paper a method of training set selection, in case of low data availability, is proposed and experimentally evaluated with the use of k-NN and neural classifiers. Application of proposed approach visibly improves the results compared to the case of training without postulated enhancements. Moreover, a new measure of distance between events in the pattern space is proposed and tested with ...

As a step towards neural realization of various similarity based algorithms k-NN method has been extended to weighted nearest neighbor scheme. Experiments show that for some datasets significant improvements are obtained. As an alternative to the minimization procedures a best–first search weighted nearest neighbor scheme has been implemented. A feature selection method for k-NN, based on a var...

The nearest neighbour (NN) rule is widely used in pattern recognition tasks due to its simplicity and its good behaviour. Many fast NN search algorithms have been developed during last years. However, in some classification tasks an exact NN search is too slow, and a way to quicken the search is required. To face these tasks it is possible to use approximate NN search, which usually increases e...

Nonparametric methods have become increasingly popular in statistics and probabilistic AI communities. One well-known nonparametric method is “nearestneighbor”. It uses the observations in the training set T closest in input space to a query q to form the prediction of q. Specifically, when k of the observations in T are considered, it is called k-nearest-neighbor (or k-NN). Despite its simplic...

Non-parametric k nearest neighbours (k-nn) techniques are increasingly used in forestry problems, especially in remote sensing. Parametric regression analysis has the advantage of well-known statistical theory behind it, whereas the statistical properties of k-nn are less studied. In this study, we compared the relative performance of k-nn and linear regression in an experiment. We examined the...