Rule Ensembles for Multi-target Regression

—Methods for learning decision rules are being successfully applied to many problem domains, especially where understanding and interpretation of the learned model is necessary. In many real life problems, we would like to predict multiple related (nominal or numeric) target attributes simultaneously. Methods for learning rules that predict multiple targets at once already exist, but are unfortunately based on the covering algorithm, which is not very well suited for regression problems. A better solution for regression problems may be a rule ensemble approach that transcribes an ensemble of decision trees into a large collection of rules. An optimization procedure is then used for selecting the best (and much smaller) subset of these rules, and to determine their weights. Using the rule ensembles approach we have developed a new system for learning rule ensembles for multi-target regression problems. The newly developed method was extensively evaluated and the results show that the ...

• Type: Conference
• Year: 2009
• Where: ICDM
• Authors: Timo Aho, Bernard Zenko, Saso Dzeroski
• Keywords: Data Mining, ICDM 2009, Multi-target Regression, Regression Problems, Rule Ensembles