Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Information Theory and Reliable Communication
Information Theory and Reliable Communication
Explanation-Based Generalization: A Unifying View
Machine Learning
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
DS '98 Proceedings of the First International Conference on Discovery Science
Test-Cost Sensitive Classification Based on Conditioned Loss Functions
ECML '07 Proceedings of the 18th European conference on Machine Learning
Qualitative test-cost sensitive classification
Pattern Recognition Letters
Generation of attributes for learning algorithms
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Cost sensitive classification in data mining
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Cost-Sensitive decision tree learning for forensic classification
ECML'06 Proceedings of the 17th European conference on Machine Learning
Decision tree classifiers sensitive to heterogeneous costs
Journal of Systems and Software
A competition strategy to cost-sensitive decision trees
RSKT'12 Proceedings of the 7th international conference on Rough Sets and Knowledge Technology
Information Sciences: an International Journal
| Hi-index | 0.00 |
A new decision tree learning algorithm called IDX is described. More general than existing algorithms, IDX addresses issues of decision tree quality largely overlooked in the artificial intelligence and machine learning literature. Decision tree size, error rate, and expected classification cost are just a few of the quality measures it can exploit. Furthermore, decision trees of varying quality can be induced simply by adjusting the complexity of the algorithm. Quality should be addressed during decision tree construction since retrospective pruning of the tree, or of a derived rule set, may be unable to compensate for inferior splitting decisions. The complexity of the algorithm, the basis for the heuristic it embodies, and the results of three different sets of experiments are described.