Learning Multi-label Alternating Decision Trees from Texts and Data Export

@incollection{citeulike:3392668, abstract = {Multi-label decision procedures are the target of the supervised learning algorithm we propose in this paper. Multi-label decision procedures map examples to a finite set of labels. Our learning algorithm extends Schapire and Singer's Adaboost.MH and produces sets of rules that can be viewed as trees like Alternating Decision Trees (invented by Freund and Mason). Experiments show that we take advantage of both performance and readability using boosting techniques as well as tree representations of large set of rules. Moreover, a key feature of our algorithm is the ability to handle heterogenous input data: discrete and continuous values and text data.}, author = {De Comit\'{e}, Francesco and Gilleron, R\'{e}mi and Tommasi, Marc}, citeulike-article-id = {3392668}, citeulike-linkout-0 = {http://dx.doi.org/10.1007/3-540-45065-3_4}, doi = {10.1007/3-540-45065-3_4}, journal = {Machine Learning and Data Mining in Pattern Recognition}, pages = {251--274}, posted-at = {2008-10-10 09:44:07}, priority = {2}, title = {Learning Multi-label Alternating Decision Trees from Texts and Data}, url = {http://dx.doi.org/10.1007/3-540-45065-3_4}, year = {2003} }

TY - CHAP ID - citeulike:3392668 L3 - citeulike-article-id:3392668 N2 - Multi-label decision procedures are the target of the supervised learning algorithm we propose in this paper. Multi-label decision procedures map examples to a finite set of labels. Our learning algorithm extends Schapire and Singer’s Adaboost.MH and produces sets of rules that can be viewed as trees like Alternating Decision Trees (invented by Freund and Mason). Experiments show that we take advantage of both performance and readability using boosting techniques as well as tree representations of large set of rules. Moreover, a key feature of our algorithm is the ability to handle heterogenous input data: discrete and continuous values and text data. JF - Machine Learning and Data Mining in Pattern Recognition EP - 274 TI - Learning Multi-label Alternating Decision Trees from Texts and Data SP - 251 AU - De Comité, Francesco AU - Gilleron, Rémi AU - Tommasi, Marc PY - 2003/// UR - http://dx.doi.org/10.1007/3-540-45065-3_4 ER -