On the Impact of Distance Metrics in Instance-Based Learning Algorithms
In this paper we analyze the impact of distinct distance metrics in instance-based learning algorithms. In particular, we look at the well-known 1-Nearest Neighbor (NN) algorithm and the Incremental Hypersphere Classifier (IHC) algorithm, which proved to be efficient in large-scale recognition probl...
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| Format: | article |
| Language: | eng |
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2016
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| Online Access: | http://hdl.handle.net/10314/3247 |
| Country: | Portugal |
| Oai: | oai:bdigital.ipg.pt:10314/3247 |
| Summary: | In this paper we analyze the impact of distinct distance metrics in instance-based learning algorithms. In particular, we look at the well-known 1-Nearest Neighbor (NN) algorithm and the Incremental Hypersphere Classifier (IHC) algorithm, which proved to be efficient in large-scale recognition problems and online learning. We provide a detailed empirical evaluation on fifteen datasets with several sizes and dimensionality. We then statistically show that the Euclidean and Manhattan metrics significantly yield good results in a wide range of problems. However, grid-search like methods are often desirable to determine the best matching metric depending on the problem and algorithm. |
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