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Effect of choice of dissimilarity measure on classification efficiency with nearest neighbor method

100%

Górecki T.

Discussiones Mathematicae Probability and Statistics

2005

tom 25

nr 2

217-239

In this paper we will precisely analyze the nearest neighbor method for different dissimilarity measures, classical and weighed, for which methods of distinguishing were worked out. We will propose looking for weights in the space of discriminant coordinates. Experimental results based on a number of real data sets are presented and analyzed to illustrate the benefits of the proposed methods. As classical dissimilarity measures we will use the Euclidean metric, Manhattan and post office metric. We gave the first two metrics weights and now these measures are not metrics because the triangle inequality does not hold. Howeover, it does not make them useless for the nearest neighbor classification method. Additionally, we will analyze different methods of tie-breaking.

A variant of gravitational classification

100%

Górecki T., Luczak M.

Biometrical Letters

2014

tom 51

nr 1

1-12

In this article there is proposed a new two-parametrical variant of the gravitational classification method. We use the general idea of objects' behavior in a gravity field. Classification depends on a test object's motion in a gravity field of training points. To solve this motion problem, we use a simulation method. This classifier is compared to the 1NN method, because our method tends towards it for some parameter values. Experimental results on different data sets demonstrate an improvement in efficiency and that this approach outperforms the 1NN method by providing a significant reduction in the mean classification error rate.

Ograniczanie wyników

1 Biometrical Letters

1 Discussiones Mathematicae Probability and Statistics

2 Górecki T.

1 Luczak M.

1 2014

1 2005

Wyniki wyszukiwania

Effect of choice of dissimilarity measure on classification efficiency with nearest neighbor method

A variant of gravitational classification