An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms (1995)
| Venue: | Machine Learning |
| Citations: | 81 - 5 self |
BibTeX
@INPROCEEDINGS{Wettschereck95anexperimental,
author = {Dietrich Wettschereck and Thomas G. Dietterich and Richard Sutton},
title = {An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms},
booktitle = {Machine Learning},
year = {1995},
pages = {5--28}
}
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Abstract
. Algorithms based on Nested Generalized Exemplar (NGE) theory (Salzberg, 1991) classify new data points by computing their distance to the nearest "generalized exemplar" (i.e., either a point or an axis-parallel rectangle). They combine the distance-based character of nearest neighbor (NN) classifiers with the axis-parallel rectangle representation employed in many rulelearning systems. An implementation of NGE was compared to the k-nearest neighbor (kNN) algorithm in 11 domains and found to be significantly inferior to kNN in 9 of them. Several modifications of NGE were studied to understand the cause of its poor performance. These show that its performance can be substantially improved by preventing NGE from creating overlapping rectangles, while still allowing complete nesting of rectangles. Performance can be further improved by modifying the distance metric to allow weights on each of the features (Salzberg, 1991). Best results were obtained in this study when the weights were co...
