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Rule ensembles for multi-target regression

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Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoProceedings of the 9th IEEE International Conference on Data Mining, ICDM 2009, Miami, Florida, USA, 6-9 December 2009
ToimittajatW. Wang
Sivut21-30
DOI - pysyväislinkit
TilaJulkaistu - 2009
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE INTERNATIONAL CONFERENCE ON DATA MINING -
Kesto: 1 tammikuuta 1900 → …

Conference

ConferenceIEEE INTERNATIONAL CONFERENCE ON DATA MINING
Ajanjakso1/01/00 → …

Tiivistelmä

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 accuracy of multi-target regression rule ensembles is better than the accuracy of multitarget regression trees, but somewhat worse than the accuracy of multi-target random forests. The rules are significantly more concise than random forests, and it is also possible to create very small rule sets that are still comparable in accuracy to single regression trees.

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