K-NN algorithm based on neural similarity

Author

Lazzerini, Beatrice ; Marcelloni, Francesco

Author_Institution

Dipt. di Ingegneria della Informazione, Pisa Univ., Italy

fYear

2002

fDate

2002

Firstpage

67

Lastpage

70

Abstract

The aim of this paper is to present a k-nearest neighbour (k-NN) classifier based on a neural model of the similarity measure between data. After a preliminary phase of supervised learning for similarity determination, the neural similarity measure is used to guide the k-NN rule. Experiments on both synthetic and real-world data show that the similarity-based k-NN rule outperforms the Euclidean distance-based k-NN rule.

Keywords

feedforward neural nets; learning (artificial intelligence); multilayer perceptrons; pattern classification; Euclidean distance-based k-NN rule; data similarity measure; experiments; feedforward neural network; k-NN algorithm; k-nearest neighbour classifier; multilayer perceptron; neural model; neural similarity; pattern classification; similarity-based k-NN rule; supervised learning; Application software; Data mining; Electronic mail; Euclidean distance; Feedforward neural networks; Neural networks; Phase measurement; Shape; Supervised learning; Unsupervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence Systems, 2002. (ICAIS 2002). 2002 IEEE International Conference on

Print_ISBN

0-7695-1733-1

Type

conf

DOI

10.1109/ICAIS.2002.1048054

Filename

1048054