Learning multi-label alternating decision trees from texts and data
作者： Francesco De Comité , Rémi Gilleron , Marc Tommasi
关键词: ID3 algorithm 、 Machine learning 、 Boosting (machine learning) 、 Supervised learning 、 Information Fuzzy Networks 、 Artificial intelligence 、 Computer science 、 Decision tree 、 Alternating decision tree 、 Text mining 、 Decision tree learning 、 Finite set
摘要: Multi-label decision procedures are the target of supervised learning algorithm we propose in this paper. map examples to a finite set labels. Our extends Schapire and Singer's Adaboost.MH produces sets rules that can be viewed as trees like Alternating Decision Trees (invented by Freund Mason). Experiments show take advantage both performance readability using boosting techniques well tree representations large rules. Moreover, key feature our is ability handle heterogenous input data: discrete continuous values text data.
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Thomas G. Dietterich, Yishay Mansour, Michael J. Kearns, Applying the Waek Learning Framework to Understand and Improve C4.5. international conference on machine learning. pp. 96- 104 ,(1996)
Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall, Multiclass Alternating Decision Trees Lecture Notes in Computer Science. pp. 161- 172 ,(2002) , 10.1007/3-540-36755-1_14
Ron Kohavi, Clayton Kunz, Option Decision Trees with Majority Votes international conference on machine learning. pp. 161- 169 ,(1997)
T. G. Dietterich, G. Bakiri, Solving multiclass learning problems via error-correcting output codes Journal of Artificial Intelligence Research. ,vol. 2, pp. 263- 286 ,(1994) , 10.1613/JAIR.105
Yoav Freund, Llew Mason, The Alternating Decision Tree Learning Algorithm international conference on machine learning. pp. 124- 133 ,(1999)
Yoav Freund, Robert E Schapire, A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting conference on learning theory. ,vol. 55, pp. 119- 139 ,(1997) , 10.1006/JCSS.1997.1504
Robert E. Schapire, Yoram Singer, Improved boosting algorithms using confidence-rated predictions conference on learning theory. ,vol. 37, pp. 80- 91 ,(1998) , 10.1145/279943.279960
Michael Kearns, Yishay Mansour, On the boosting ability of top-down decision tree learning algorithms symposium on the theory of computing. pp. 459- 468 ,(1996) , 10.1145/237814.237994
Robert E. Schapire, Yoram Singer, BoosTexter: A Boosting-based Systemfor Text Categorization Machine Learning. ,vol. 39, pp. 135- 168 ,(2000) , 10.1023/A:1007649029923
Reducing multiclass to binary: a unifying approach for margin classifiers Journal of Machine Learning Research. ,vol. 1, pp. 113- 141 ,(2001) , 10.1162/15324430152733133