C4.5: programs for machine learning
C4.5: programs for machine learning
EURO-DAC '92 Proceedings of the conference on European design automation
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Machine Learning by Function Decomposition
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Using Rough Sets with Heuristics for Feature Selection
Journal of Intelligent Information Systems
Data mining by attribute decomposition with semiconductor manufacturing case study
Data mining for design and manufacturing
Improving Supervised Learning by Feature Decomposition
FoIKS '02 Proceedings of the Second International Symposium on Foundations of Information and Knowledge Systems
Function Decomposition in Machine Learning
Machine Learning and Its Applications, Advanced Lectures
Feature construction for reduction of tabular knowledge-based systems
Information Sciences—Informatics and Computer Science: An International Journal
Decision-tree instance-space decomposition with grouped gain-ratio
Information Sciences: an International Journal
From machine learning to knowledge discovery: Survey of preprocessing and postprocessing
Intelligent Data Analysis
Computational Statistics & Data Analysis
Artificial Intelligence Review
Develop multi-hierarchy classification model: rough set based feature decomposition method
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
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Function decomposition decomposes a dataset to an equivalent hierarchy of less complex datasets. In this article, we show that function decomposition can discover a hierarchy of new features and construct features to be added to the original dataset. An advantage of function decomposition is its ability to identify appropriate subsets of existing features and discover functions that map each subset to a new feature. These functions are induced from examples and are not predefined in any way.