HammingNN classifier: results with various datasets
2012-12-19
This table presents results of using the HammingNN classifier with a variety of publicly available datasets that are frequently used for development and testing of classifier paradigms. For comparison purposes, I’ve included results from 3 other classifier paradigms.
Note: you may need to widen your browser window to view all the columns.
| Data Set | Cases | Attributes | Classes | HammingNN leave-one-out |
HammingNN 10-fold |
Parameters | XCSTS | C4.5 | Naive Bayes |
| Anneal | 898 | 32D 6C | 5 | 99.67% | 99.496% | n=21 i=28 k=2 | 97.7% | 98.6 | 86.6 |
| Breast cancer Wisconsin | 699 | 9D | 2 | 96.567% | 96.383% | i=8 j=3 | 95.9 | 94.5 | 96.0 |
| BUPA | 345 | 6C | 2 | 70.72% | 69.67% | n=15 | 67.1 | 65.0 | 54.8 |
| Glass | 214 | 9C | 7 | 76.64% | 75.45% | g n=19 i=5 | 71.8 | 67.4 | 48.1 |
| Ionosphere | 351 | 32C | 2 | 93.447% | 92.829% | n=12 i=23 | 90.1 | 90.0 | 82.5 |
| Iris | 150 | 4C | 3 | 98.000% | 97.960% | i=2 n=3 | 94.7 | 94.8 | 95.5 |
| Leukemia | 72 | 5147C | 2 | 97.222% | 95.028% | n=8 i=11 | |||
| Mushroom | 8124 | 22D | 2 | 100% | 100% | i=7 | 100.0 | 100.0 | 95.8 |
| Prostata | 102 | 12533C | 2 | 94.118% | 92.451% | n=6 i=3 | |||
| Soybean | 683 | 35D | 19 | 94.290% | 93.634% | z i=25 | 85.1 | 91.9 | 92.8 |
| Vehicle | 846 | 18C | 4 | 70.567% | 70.051% | i=18 n=13 | 74.1 | 72.4 | 45.1 |
| Vowel | 990 | 10C | 11 | 98.687% | 98.087% | g n=13 | 66.0 | 80.1 | 63.2 |
| Wine | 178 | 13C | 3 | 97.191% | 96.708% | n=5 | 95.6 | 93.3 | 97.2 |
| Yeast | 1484 | 8C | 10 | 58.154% | 57.023% | u=5 i=6 | |||
| Zoo | 101 | 16D | 7 | 98.02% | 97.911% | i=12 | 95.1 | 93.0 | 95.7 |
Data Sets: from the UIC Machine Learning Repository; I used the versions of these datasets downloaded from the Orange website: http://orange.biolab.si/datasets.psp
Attributes: D = discrete (ie nominal or ordinal or categorical); C = continuous-valued.
Parameters:
- i = number of attributes used;
- n = number of “slices” for continuous attributes;
- u = upper limit for number of slices (when number of slices is automatically calculated);
- j, k, z, g: parameters specifying, respectively, depth value, variable margin, one bit per class for discrete attributes, graded bits for continuous attributes.
The results in the XCSTS, C4.5, and Naive Bayes columns are from Table 9.3 in the book: Butz MV. Rule-based evolutionary online learning systems : a principled approach to LCS analysis and design. Berlin: Springer; 2006:xxi, 266.
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