In the field of statistical discrimination nearest neighbor methods are a well known, quite simple but successful nonparametric classification tool. In higher dimensions, however, predictive power normally deteriorates. In general, if some covariates are assumed to be noise variables, variable selection is a promising approach. The paper’s main focus is on the development and evaluation of a ne...

An efficient approach to Nearest Neighbor classification is presented, which improves performance by exploiting the ability of superscalar processors to issue multiple instructions per cycle and by using the memory hierarchy adequately. This is accomplished by the use of floating-point arithmetic which outperforms integer arithmetic, and block (tiled) algorithms which exploit the data locality ...

Multi-view classification is a machine learning methodology when patterns or objects of interest are represented by a set of different views (sets of features) rather than the union of all views. In this paper, multiple views are employed in ensembles of nearest neighbor classifiers where they demonstrate promising results in classifying a challenging data set of protein folds. In particular, u...

The k-nearest neighbor (k-NN) is one of the most popular algorithms used for classification in various fields of pattern recognition & data mining problems. In k-nearest neighbor classification, the result of a new instance query is classified based on the majority of k-nearest neighbors. Recently researchers have begun paying attention to combining a set of individual k-NN classifiers, each us...

The nearest neighbor (NN) classifier is well suited for generic object recognition. However, it requires storing the complete training data, and classification time is linear in the amount of data. There are several approaches to improve runtime and/or memory requirements of nearest neighbor methods: Thinning methods select and store only part of the training data for the classifier. Efficient ...

Recently, some feature extraction methods have been developed by representing images with matrix directly, however few of them are proposed to improve accuracy of classification directly. In this paper, a novel feature extraction method, twodimensional nearest neighbor discriminant analysis(2DNNDA), is proposed from the view of the nearest neighbor classification, which makes use of the matrix ...