Table 1. Summary of feature selection results for the TopN algorithm. Each column holds number of attributes that were included in N most important attributes at least K times, K is given in column head. The last column holds the number of attributes that were included at most five times and can be considered noise.

| Common attributes = 30 ≥ 27 ≥ 24 ≥ 21 ≥ 18 ≥ 15 ≥ 1 ≤ 5 TAD TOP100 16 22 28 40 53 69 393 224 TOP90 16 22 26 37 47 62 368 219 TOP80 14 19 26 29 43 54 331 200 TOP70 12 18 25 28 35 48 302 189 TOP60 9 18 21 26 32 38 250 155 TOP50 7 17 20 22 28 34 202 122 TOP40 3 14 17 21 23 29 160 97 TOP30 1 10 15 19 20 23 125 80 TOP20 1 5 8 13 15 18 74 46 TOP10 1 1 1 5 7 8 42 25 TAK TOP100 11 27 42 53 59 70 443 295 TOP90 11 25 38 47 56 66 397 264 TOP80 11 23 33 44 54 59 333 211 TOP70 10 20 28 42 46 55 283 176 TOP60 8 16 23 34 42 49 241 155 TOP50 7 13 18 27 34 45 200 122 TOP40 5 10 14 18 28 37 154 97 TOP30 4 8 11 14 15 19 112 63 TOP20 2 5 7 8 12 14 82 48 TOP10 2 3 4 5 5 7 43 28   |
| |

## 3 Results 3.1 Feature Selection

The simple feature selection procedure generates results that are highly nonreplicable between different samples, as can be seen in Table 1, even though the classification results themselves are rather stable, as can be seen in Figure 2.

The number of attributes that are found in all or nearly all iterations is low. For example, only one attribute is consistently found across all 30 iterations in the TOP30 variant for TAD system, whereas 125 attributes were included at least once in the TOP30 set in any iteration. Moreover, only 5 attributes are found in at least 90% of iterations and even at the consensus level as low as 50% only 23 commonly important attributes are found..

The stability of feature selection does not influence highly the results of classification. Once the classifier has sufficiently large number of features to build model, the results are not improving despite increasing number of variables that are included in all iterations. Interestingly, it seems that the number of attributes that are relatively highly important in all sub-samples is limited. The number of