In an earlier paper, we introduced an ensemble model called ESGNN (ensemble self-generating neural network) which can be used to reduce the error for classification and chaotic time series prediction. Although this model can obtain the high accuracy than a single SGNN, the computational cost increase in proportion to the number of SGNN in an ensemble. In this paper, we propose a new pruning SGN...

Nearest Neighbor (NN) searching is a challenging problem in data management and has been widely studied in data mining, pattern recognition and computational geometry. The goal of NN searching is efficiently reporting the nearest data to a given object as a query. In most of the studies both the data and query are assumed to be precise, however, due to the real applications of NN searching, suc...

A k-nearest neighbor (kNN) query determines the k nearest points, using distance metrics, from a given location. An all k-nearest neighbor (AkNN) query constitutes a variation of a kNN query and retrieves the k nearest points for each point inside a database. Their main usage resonates in spatial databases and they consist the backbone of many location-based applications and not only. In this w...

ss as: Pa aud Un Abstract Electroencephalogram (EEG) signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifie...

This paper presents a novel Nearest Neighbor (NN) classifier. NN classification is a well studied method for pattern classification having the following properties; * it performs maximum-margin classification and achieves less than the twice of ideal Bayesian error, * it does not require the knowledge on pattern distributions, kernel functions or base classifiers, and * it can naturally be appl...

The nearest neighbor (NN) approach is a powerfd nonparametric technique for pattern classification tasks. In this paper, algorithms for prototype reduction, hierarchical prototype organization and fast NN search are described. To remove redundant category prototypes and to avoid redundant comparisons, the algorithms exploit geometrical information of a given prototype set which is represented a...

In this paper, we propose a thorough investigation of a nearest neighbor rule which we call the “Symmetric Nearest Neighbor (sNN) rule”. Basically, it symmetrises the classical nearest neighbor relationship from which are computed the points voting for some instances. Experiments on 29 datasets, most of which are readily available, show that the method significantly outperforms the traditional ...