Scientific discovery: computational explorations of the creative process
Scientific discovery: computational explorations of the creative process
A constructive induction framework
Proceedings of the sixth international workshop on Machine learning
Machine Learning
Computers in the study of learning: BOGART—A discovery and induction program for games
ACM '65 Proceedings of the 1965 20th national conference
A Preliminary Investigation of Modified XCS as a Generic Data Mining Tool
IWLCS '01 Revised Papers from the 4th International Workshop on Advances in Learning Classifier Systems
Convex Hulls in Concept Induction
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
Feature Transformation and Multivariate Decision Tree Induction
DS '98 Proceedings of the First International Conference on Discovery Science
Feature construction for reduction of tabular knowledge-based systems
Information Sciences—Informatics and Computer Science: An International Journal
Computers and Electronics in Agriculture
Iterative feature construction for improving inductive learning algorithms
Expert Systems with Applications: An International Journal
On preprocessing data for financial credit risk evaluation
Expert Systems with Applications: An International Journal
Constructive induction of features for planning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Myths and legends in learning classification rules
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Adding domain knowledge to SBL through feature construction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Constructive induction on domain information
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Learning to learn decision trees
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
COGIN: symbolic induction with genetic algorithms
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Generation of attributes for learning algorithms
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Breeding value classification in manchego sheep: a study of attribute selection and construction
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Improving classifier performance by knowledge-driven data preparation
ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
An approach to dimensionality reduction in time series
Information Sciences: an International Journal
On the feature extraction in discrete space
Pattern Recognition
Inferring ECA-based rules for ambient intelligence using evolutionary feature extraction
Journal of Ambient Intelligence and Smart Environments
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Selective induction techniques perform poorly when the features are inappropriate for the target concept. One solution is to have the learning system construct new features automatically; unfortunately feature construction is a difficult and poorly understood problem. In this paper we present a definition of feature construction in concept learning, and offer a framework for its study based on four aspects: detection, selection, generalization, and evaluation. This framework is used in the analysis of existing learning systems and as the basis for the design of a new system, CITRE. CITRE performs feature construction using decision trees and simple domain knowledge as constructive biases. Initial results on a set of spatial-dependent problems suggest the importance of domain knowledge and feature generalization, i.e., constructive induction.