This paper describes, in detail, several greedy heuristics for construction of decision trees. We study the number of terminal nodes of decision trees, which is closely related with the cardinality of the set of rules corresponding to the tree. We compare these heuristics empirically for two different types of datasets (datasets acquired from UCI ML Repository and randomly generated data) as well as compare with the optimal results obtained using dynamic programming method.
|Original language||English (US)|
|Title of host publication||2014 IEEE International Conference on Granular Computing (GrC)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||4|
|State||Published - Oct 2014|