Experimental comparison of human and machine learning formalisms
Proceedings of the sixth international workshop on Machine learning
Indirect relevance and bias in inductive concept-learning
Knowledge Acquisition
Rule induction with CN2: some recent improvements
EWSL-91 Proceedings of the European working session on learning on Machine learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
C4.5: programs for machine learning
C4.5: programs for machine learning
The power of sampling in knowledge discovery
PODS '94 Proceedings of the thirteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Approximate inference of functional dependencies from relations
ICDT '92 Selected papers of the fourth international conference on Database theory
Evaluation and Selection of Biases in Machine Learning
Machine Learning - Special issue on bias evaluation and selection
Machine Learning
Pruning Algorithms for Rule Learning
Machine Learning
Selective Sampling Using the Query by Committee Algorithm
Machine Learning
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Learning to classify text from labeled and unlabeled documents
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
Learning Logical Definitions from Relations
Machine Learning
Machine Learning
Conditions for Occam's Razor Applicability and Noise Elimination
ECML '97 Proceedings of the 9th European Conference on Machine Learning
Peepholing: Choosing Attributes Efficiently for Megainduction
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Employing EM and Pool-Based Active Learning for Text Classification
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
How to shift bias: Lessons from the baldwin effect
Evolutionary Computation
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Tractable induction and classification in first order logic via stochastic matching
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Active learning with committees for text categorization
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A simple, fast, and effective rule learner
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Data Reduction Using Multiple Models Integration
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Efficient Data Mining by Active Learning
Progress in Discovery Science, Final Report of the Japanese Discovery Science Project
Progressive rademacher sampling
Eighteenth national conference on Artificial intelligence
Learning Minesweeper with multirelational learning
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Rule stacking: an approach for compressing an ensemble of rule sets into a single classifier
DS'11 Proceedings of the 14th international conference on Discovery science
Scalable inductive learning on partitioned data
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Information Sciences: an International Journal
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In this paper we re-investigate windowing for rule learning algorithms. We show that, contrary to previous results for decision tree learning, windowing can in fact achieve significant run-time gains in noise-free domains and explain the different behaviour of rule learning algorithms by the fact that they learn each rule independently. The main contribution of this paper is integrative windowing, a new type of algorithm that further exploits this property by integrating good rules into the final theory right after they have been discovered. Thus it avoids re-learning these rules in subsequent iterations of the windowing process. Experimental evidence in a variety of noise-free domains shows that integrative windowing can in fact achieve substantial run-time gains. Furthermore, we discuss the problem of noise in windowing and present an algorithm that is able to archieve run-time gains in a set of experiments in a simple domain with artificial noise.