

![0_image_0.png](0_image_0.png)

| Original Dataset   |
|--------------------|
| Majority Filter    |

Ta ble Class distribution for the fire seventy dataset with and without filtering averaged over ten runs

| Land Cover   | Credit   | Ro ad   | Se ne   | Fire Severity   |      |    |      |    |       |    |
|--------------|----------|---------|---------|-----------------|------|----|------|----|-------|----|
| Noise        | None     | CF      | None    | CF              | None | CF | None | CF | No ne | CF |

remain flat across all noise levels In the traditional use of this dataset predicting fire versus no fire  accuracies of were observed for regression prediction while for the task of versus high risk days the accuracy was predicting low Krusel Dowe In summary for this dataset we conjecture that either the features are inadequate to discriminate classes or the labels in the original data contain a degree of subjectivity that makes it impossible to create an accurate filter

## 4.5  Effect Of Filtering On Tree Size

Applying filters to the training data leads to substantially smaller decision trees Table reports the number of leaves in decision trees produced from the consensus filtered and the noise the filtered data creates trees with fewer leaves than trees unfiltered data 9 For estimated from the original dataset For the road segmentation and fire severity datasets noise the trees produced from the filtered data have fewer leaves than the even at to one produced from the original dataset at noise  This effect was also observed by John and attributed to Robust C4.5's ability to remove  confusing" instances from the training data thereby reducing the size of the learned decision trees  Oates and Jensen showed empirically that for many datasets there is a linear relationship between tree size and the number of training instances randomly increasing the number of training instances has the effect of increasing tree size even when pruning is applied 10 Their analysis of Robust C4.5shows that of the decrease in tree size is attributable to reduction