Maximizing the Area under the ROC Curve with Decision Lists and Rule Sets
作者： Henrik Boström
DOI:
关键词: Area under the roc curve 、 Artificial intelligence 、 Decision list 、 Representation (mathematics) 、 Pattern recognition 、 Mathematics 、 Rule sets
摘要: Decision lists (or ordered rule sets) have two attractive properties compared to unordered sets: they require a simpler classi¯cation procedure and allow for more compact representation ...
参考文章(22)
Henrik Boström, Pruning and Exclusion Criteria for Unordered Incremental Reduced Error Pruning european conference on machine learning. ,(2004)
Tom Fawcett, ROC Graphs: Notes and Practical Considerations for Data Mining Researchers ,(2003)
Peter A. Flach, Ronaldo C. Prati, ROCCER: A ROC convex hull rule learning algorithm ,(2004)
Foster Provost, Pedro Domingos, Tree Induction for Probability-Based Ranking Machine Learning. ,vol. 52, pp. 199- 215 ,(2003) , 10.1023/A:1024099825458
Foster Provost, R Fawcett, T, Kohavi, The Case against Accuracy Estimation for Comparing Induction Algorithms international conference on machine learning. pp. 445- 453 ,(1998)
Ian H. Witten, Eibe Frank, Generating Accurate Rule Sets Without Global Optimization international conference on machine learning. pp. 144- 151 ,(1998)
Bojan Cestnik, Ivan Bratko, On estimating probabilities in tree pruning Lecture Notes in Computer Science. pp. 138- 150 ,(1991) , 10.1007/BFB0017010
William W. Cohen, Fast Effective Rule Induction Machine Learning Proceedings 1995. pp. 115- 123 ,(1995) , 10.1016/B978-1-55860-377-6.50023-2
Johannes Fürnkranz, Separate-and-Conquer Rule Learning Artificial Intelligence Review. ,vol. 13, pp. 3- 54 ,(1999) , 10.1023/A:1006524209794
Tony Lindgren, Henrik Boström, Resolving Rule Conflicts with Double Induction Advances in Intelligent Data Analysis V. ,vol. 8, pp. 60- 67 ,(2003) , 10.1007/978-3-540-45231-7_6