Table 1.
Comparison of the Parzen-Rosenblatt Window and Naïve Bayes methods
| Partition No. | PRW Rank | NB Rank |
|---|---|---|
| 1 | 17.049 | 74.104 |
| 2 | 16.343 | 76.251 |
| 3 | 18.424 | 79.078 |
| 4 | 16.212 | 73.539 |
| 5 | 17.339 | 73.535 |
| 6 | 18.630 | 77.244 |
| 7 | 20.694 | 78.560 |
| 8 | 18.870 | 74.464 |
| 9 | 16.584 | 76.235 |
| 10 | 18.200 | 78.077 |
| Average | 17.835 | 76.109 |
Average ranks of known targets as predicted in the 10-fold cross-validation by the Parzen-Rosenblatt Window [18] and by a Naïve Bayes method [19]. The Parzen-Rosenblatt Window, using h = 2-3, consistently assigns better ranks to the known targets, its predicted ranks being numerically smaller by a factor of 4.3.