
## 4.1.5 Fire Danger Prediction

The goal of this dataset is to rank fires in terms of their severity on a scale of A
label of indicates no fire occurred on that day and labels      represent fires from minor The ranking of any event by humans is necessarily subjective in nature to severe Furthermore since fires are events that occur over time the chance of inconsistent ranking increases with the chance that a different person may do the ranking This dataset was compiled on bush fire activity in the mallee vegetation area of north west Victoria  Australia  Dowe   Krusel The dataset contains b a observations  days  each described by ten features that measure the maximum tempera ture the number of days since it last rained  a drought index  the temperature at   pm  the wet bulb temperature the wind speed the relative humidity the forest fire danger index the air pressure and the grass fire danger In our experiments we tried to predict the exact fire severity whereas historically the most common use of this data is to turn it into a binary prediction problem that distinguishes between no fire class and fire  classes Because it seems highly unlikely that one would erroneously label a fire free day as having a fire and vice versa we introduced class confusions among the pairs and

## 4.2 Experiment Al  Met Ho D

As described above to test the single algonthm majority vote and consensus procedures we introduced random noise into the training data between pairs of classes that are most likely to be confused in the original labels In this way we have simulated the type of labeling error that is common to each domain The pairs of classes to which error was introduced for each domain were described in the previous section For each of ten runs  each dataset was randomly divided into a training set and set After the data was split into independent train and test sets  we then a testing corrupted the training data by introducing labeling errors using noise levels ranging from noise For a noise level x an individual observation whose class was one of the to identified problematic pairs had an x chance of being corrupted For example in the land cover domain an instance from class bare ground has an x chance of being changed to shrubs and bare ground and an instance from class has an x chance of being class changed to class    Using this method the percentage of the entire training set that was corrupted may be less than x for multi class problems because only some pairs of classes are considered problematic The actual percentage of noise in the corrupted training data is reported in tables that present the experimental results For each noise level  we compared the average predictive accuracy of classifiers trained using filtered versus unfiltered data  For each of the ten runs that make up the average we used a four fold cross validation to filter the corrupted instances from the training data  To assess the ability of the single algonthm  majority and consensus filter methods to identify the corrupted instances we then ran each of the learning algonthms twice  first using the unfiltered dataset then using the filtered dataset