Local distance-based classification
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: 18 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-Students 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.
classification distance measure k-nn similarity machine learning