Learning decision rules in noisy domains
Proceedings of Expert Systems '86, The 6Th Annual Technical Conference on Research and development in expert systems III
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Boolean Feature Discovery in Empirical Learning
Machine Learning
Experimental comparison of human and machine learning formalisms
Proceedings of the sixth international workshop on Machine learning
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
Learning nonrecursive definitions of relations with LINUS
EWSL-91 Proceedings of the European working session on learning on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
FOSSIL: a robust relational learner
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Machine Learning
Decision Tree Pruning as a Search in the State Space
ECML '93 Proceedings of the European Conference on Machine Learning
A Tight Integration of Pruning and Learning (Extended Abstract)
ECML '95 Proceedings of the 8th European Conference on Machine Learning
The Complexity of Batch Approaches to Reduced Error Rule Set Induction
PRICAI '96 Proceedings of the 4th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Binary Rule Generation via Hamming Clustering
IEEE Transactions on Knowledge and Data Engineering
A Scalable Algorithm for Rule Post-pruning of Large Decision Trees
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
A Pathology of Bottom-Up Hill-Climbing in Inductive Rule Learning
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Exploiting Structural Information for Text Classification on the WWW
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
The Journal of Machine Learning Research
Rule Extraction from Support Vector Machines: A Sequential Covering Approach
IEEE Transactions on Knowledge and Data Engineering
Machine learning: a review of classification and combining techniques
Artificial Intelligence Review
A Model to Study Phase Transition and Plateaus in Relational Learning
ILP '08 Proceedings of the 18th international conference on Inductive Logic Programming
Empirical Study of Relational Learning Algorithms in the Phase Transition Framework
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Journal of Artificial Intelligence Research
An Empirical Comparison of Probability Estimation Techniques for Probabilistic Rules
DS '09 Proceedings of the 12th International Conference on Discovery Science
Relabeling algorithm for retrieval of noisy instances and improving prediction quality
Computers in Biology and Medicine
Noise-resistant incremental relational learning using possible worlds
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
Performance Analysis of Class Noise Detection Algorithms
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
An ensemble of decision cluster crotches for classification of high dimensional data
Knowledge-Based Systems
Ensemble-based noise detection: noise ranking and visual performance evaluation
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Pre-pruning and Post-pruning are two standard techniques for handlingnoise in decision tree learning. Pre-pruning deals with noise duringlearning, while post-pruning addresses this problem after anoverfitting theory has been learned. We first review severaladaptations of pre- and post-pruning techniques forseparate-and-conquer rule learning algorithms and discuss somefundamental problems. The primary goal of this paper is to show howto solve these problems with two new algorithms that combine andintegrate pre- and post-pruning.