Constructing conjunctions using systematic search on decision trees Export

@article{citeulike:1760787, abstract = {This paper investigates a dynamic path-based method for constructing conjunctions as new attributes for decision tree learning. It searches for conditions (attribute-value pairs) from paths to form new attributes. Compared with other hypothesis-driven new attribute construction methods, the new idea of this method is that it carries out a systematic search with pruning over each path of a tree to select conditions for generating a conjunction. Therefore, conditions for constructing new attributes are dynamically decided during the search. Empirically, evaluation in a set of artificial and real-world domains shows that the dynamic path-based method can improve the performance of selective decision tree learning in terms of both higher prediction accuracy and lower theory complexity. In addition, it shows some performance advantages over a fixed path-based method and a fixed rule-based method for learning decision trees.}, author = {Zheng, Zijian}, booktitle = {KDD: Techniques and Applications}, citeulike-article-id = {1760787}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/S0950-7051(98)00036-7}, citeulike-linkout-1 = {http://www.sciencedirect.com/science/article/B6V0P-3TC1WPX-4/2/a3ca4d2135e5f1d4184dfc874257aeae}, doi = {10.1016/S0950-7051(98)00036-7}, journal = {Knowledge-Based Systems}, keywords = {classification, learning}, month = {May}, number = {7}, pages = {421--430}, posted-at = {2007-10-12 16:04:09}, priority = {5}, title = {Constructing conjunctions using systematic search on decision trees}, url = {http://dx.doi.org/10.1016/S0950-7051(98)00036-7}, volume = {10}, year = {1998} }

TY - JOUR ID - citeulike:1760787 L3 - citeulike-article-id:1760787 N2 - This paper investigates a dynamic path-based method for constructing conjunctions as new attributes for decision tree learning. It searches for conditions (attribute-value pairs) from paths to form new attributes. Compared with other hypothesis-driven new attribute construction methods, the new idea of this method is that it carries out a systematic search with pruning over each path of a tree to select conditions for generating a conjunction. Therefore, conditions for constructing new attributes are dynamically decided during the search. Empirically, evaluation in a set of artificial and real-world domains shows that the dynamic path-based method can improve the performance of selective decision tree learning in terms of both higher prediction accuracy and lower theory complexity. In addition, it shows some performance advantages over a fixed path-based method and a fixed rule-based method for learning decision trees. IS - 7 JF - Knowledge-Based Systems EP - 430 TI - Constructing conjunctions using systematic search on decision trees VL - 10 T2 - KDD: Techniques and Applications SP - 421 KW - classification KW - learning AU - Zheng, Zijian PY - 1998/05// UR - http://dx.doi.org/10.1016/S0950-7051(98)00036-7 ER -