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Machine Learning - Special issue on multistrategy learning
Separate-and-Conquer Rule Learning
Artificial Intelligence Review
What do Constructive Learners Really Learn?
Artificial Intelligence Review
Constructing X-of-N Attributes for Decision Tree Learning
Machine Learning
Feature Generation Using General Constructor Functions
Machine Learning
Representation Operators and Computation
Minds and Machines
Feature Transformation and Subset Selection
IEEE Intelligent Systems
Data-Driven Constructive Induction
IEEE Intelligent Systems
KIDS: An Iterative Algorithm to Organize Relational Knowledge
EKAW '00 Proceedings of the 12th European Workshop on Knowledge Acquisition, Modeling and Management
A Genetic Programming Ecosystem
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
MLDM '99 Proceedings of the First International Workshop on Machine Learning and Data Mining in Pattern Recognition
Abstractions for Knowledge Organization of Relational Descriptions
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
Visualizing Decision Table Classifiers
INFOVIS '98 Proceedings of the 1998 IEEE Symposium on Information Visualization
Machine Learning and Its Applications, Advanced Lectures
Feature Transformation and Multivariate Decision Tree Induction
DS '98 Proceedings of the First International Conference on Discovery Science
Data reduction: feature aggregation
Handbook of data mining and knowledge discovery
Perceptual Learning and Abstraction in Machine Learning
ICCI '03 Proceedings of the 2nd IEEE International Conference on Cognitive Informatics
Experiments with Learning Parsing Heuristics
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 1
Pareto-optimal patterns in logical analysis of data
Discrete Applied Mathematics - Discrete mathematics & data mining (DM & DM)
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Constructing nominal X-of-N attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Pareto-optimal patterns in logical analysis of data
Discrete Applied Mathematics
Medical datasets analysis: a constructive induction approach
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
A new hybrid method of generation of decision rules using the constructive induction mechanism
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
On-Line learning of decision trees in problems with unknown dynamics
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Improving VG-RAM neural networks performance using knowledge correlation
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Approximate boolean reasoning: foundations and applications in data mining
Transactions on Rough Sets V
Seeking Knowledge in the Deluge of Facts
Fundamenta Informaticae
Rough Set Based Reasoning About Changes
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P)
An approach to dimensionality reduction in time series
Information Sciences: an International Journal
Redefinition of Decision Rules Based on the Importance of Elementary Conditions Evaluation
Fundamenta Informaticae
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The proposed method for constructive induction searches for concept descriptions in a representation space that is being iteratively improved. In each iteration, the system learns concept description from training examples projected into a newly constructed representation space, using an Aq algorithm-based inductive learning system (AQ15). The learned description is analyzed to determine desirable problem-oriented modifications of the representation space. These modifications include generating new attributes, removing redundant or insignificant ones, and/or agglomerating attribute values into larger units. New attributes are constructed by assigning names to groups of the best-performing characteristic rules for each decision class, and then are used to define the representation space for the next iteration. This iterative process repeats until the created hypotheses satisfy a stopping criterion. In several experiments on learning discrete functions, the developed AQ17-HCI system consistently outperformed, in terms of the prediction accuracy on new examples, all systems that it was compared to, including the AQ15 rule learning system, GREEDY3 and GROVE decision-list learning systems, and REDWOOD and FRINGE decision-tree learning systems. Although the proposed method was developed for the Aq-based rule learning system, it can potentially be adapted to any other inductive learning system. In this sense, it represents a universal new approach to constructive induction.