Rule-based classification of heavy vehicle operations

Abstract: The problem explored in this thesis is a supervised classification problem. Input data consists of operational and manufacturing data of a truck. The output denotes its operation, i.e. its basic utility and usage pattern, such as “Long distance” or “On and off-road”. In order to understand the distinction between the operation categories in practice, we look at interpretable classifiers. The examined classifiers are treeand rule-based classifiers, as they are the most interpretable. These include random forest, decision tree, and a classifier called inTrees, a method that summarizes a random forest using rules. In addition, a suggested method is examined. The suggested method works similarly to inTrees, but differs in the rule selection step. The question is whether this suggested method is better than inTrees in terms of interpretability, and how well both of them perform in comparison to a decision tree and a random forest. Another question regards the operation category of trucks, and whether they can be successfully distinguished using these methods.In order to compare the methods, their balanced accuracy, number of rules and other measures are recorded for the truck data set and additional data sets. Additional data sets are used to get a more exhaustive comparison between the methods.The suggested method does not outperform inTrees, and frequently uses three to four times as many rules to achieve the same accuracy on a given data set. Results indicate that the suggested method could perform more similarly to inTrees, given a different form of hyperparameter tuning. Additionally, it is shown that using interpretable classifiers rather than a random forest means we use less than one percent of the rules, at the cost of a loss of 10 percentage points in balanced accuracy.