Sainin, Mohd Shamrie and Alfred, Rayner (2010) Nearest neighbour distance matrix classification. In: Advanced Data Mining and Applications. Lecture Notes in Computer Science, 6440 . Springer, pp. 114-124. ISBN 978-3-642-17315-8

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Abstract

A distance based classification is one of the popular methods for classifying instances using a point-to-point distance based on the nearest neighbour or k-NEAREST NEIGHBOUR (k-NN).The representation of distance measure can be one of the various measures available (e.g. Euclidean distance, Manhattan distance, Mahalanobis distance or other specific distance measures).In this paper, we propose a modified nearest neighbour method called Nearest Neighbour Distance Matrix (NNDM) for classification based on unsupervised and supervised distance matrix.In the proposed NNDM method, an Euclidean distance method coupled with a distance loss function is used to create a distance matrix. In our approach, distances of each instance to the rest of the training instances data will be used to create the training distance matrix (TADM). Then, the TADM will be used to classify a new instance.In supervised NNDM, two instances that belong to different classes will be pushed apart from each other. This is to ensure that the instances that are located next to each other belong to the same class. Based on the experimental results, we found that the trained distance matrix yields reasonable performance in classification.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	College of Arts and Sciences
Depositing User:	Mr. Mohd. Shamrie Sainin
Date Deposited:	23 Oct 2014 07:36
Last Modified:	23 Oct 2014 07:36
URI:	https://repo.uum.edu.my/id/eprint/12419

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