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Local distance-based classification
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DOI：10.1016/j.knosys.2008.03.050.png)
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摘要
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In this paper, we have introduced a new method in which every training point learns what is happening in its neighborhood. So, a hyperplane is learned and associated to each point. With this hyperplane we can define the bands distance, a distance measure that bring closer or move away points depending on its classes. We have used this new distance in classification tasks and have performed tests over 68 datasets: IS well-known UCI-Repository datasets, one private dataset, and 49 ad hoc synthetic datasets. We have used 10-fold cross-validation and, in order to compare the results of the classifiers, we have considered the mean accuracy and have also performed a paired two-tailored t-Student's test with a significance level of 95%. The results are encouraging and confirm the good behavior of the new proposed classification method. The bands distance has obtained the best overall results with 1-NN and k-NN classifiers when compared with other distances. Finally, we extract conclusions and outline some lines of future work. (c) 2008 Elsevier B.V. All rights reserved.
Keyword:
Distance measure
k-NN
Classification
Similarity
Machine learning
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4.5W
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