000126292 001__ 126292
000126292 005__ 20190316234329.0
000126292 037__ $$aSTUDENT
000126292 245__ $$aDecision Tree Learning for Drools
000126292 269__ $$a2008
000126292 260__ $$c2008
000126292 336__ $$aStudent Projects
000126292 500__ $$aThis is a corrected version, submitted 6 September 2008
000126292 520__ $$aDecision trees can be used to represent a large number of expert system rules in a compact way. We describe machine learning algorithms for learning decision trees. We have implemented the algorithms, including bagging and boosting techniques. We have deployed the algorithms in the context of the JBoss Drools rule engine. We present experimental results evaluating the impact of bagging and boosting techniques on the classification accuracy and sizes of trees for several publicly available data sets.
000126292 6531_ $$adecision tree
000126292 6531_ $$abagging
000126292 6531_ $$aboosting
000126292 6531_ $$amachine learning
000126292 6531_ $$adrools
000126292 700__ $$aOguz, Gizil
000126292 720_2 $$aProctor, Mark$$edir.
000126292 720_2 $$0240031$$aKuncak, Viktor$$edir.$$g177241
000126292 8564_ $$s1544093$$uhttps://infoscience.epfl.ch/record/126292/files/oguz-thesis_final.pdf$$zn/a
000126292 909C0 $$0252019$$pLARA$$xU11739
000126292 909CO $$ooai:infoscience.tind.io:126292$$pIC$$qGLOBAL_SET
000126292 937__ $$aLARA-STUDENT-2008-003
000126292 973__ $$aEPFL$$sPUBLISHED
000126292 980__ $$aSTUDENT$$bMASTERS