Boolean Feature Discovery in Empirical Learning
Machine Learning
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Automatic feature generation for problem solving systems
ML92 Proceedings of the ninth international workshop on Machine learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Rule Induction with CN2: Some Recent Improvements
EWSL '91 Proceedings of the European Working Session on Machine Learning
Constructive induction on decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Generating better decision trees
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Complex concept acquisition through directed search and feature caching
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 2
A scheme for feature construction and a comparison of empirical methods
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Data-Driven Constructive Induction
IEEE Intelligent Systems
Learning and Exploiting Relative Weaknesses of Opponent Agents
Autonomous Agents and Multi-Agent Systems
Constructive induction and genetic algorithms for learning concepts with complex interaction
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Dynamic Aggregation of Relational Attributes Based on Feature Construction
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
A real-coded genetic algorithm for constructive induction
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Genetic programming for attribute construction in data mining
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A fuzzy-GA wrapper-based constructive induction model
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
Improving business rating predictions using graph based features
Proceedings of the 19th international conference on Intelligent User Interfaces
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Inductive algorithms rely strongly on their representational biases. Constructive induction can mitigate representational inadequacies. This paper introduces the notion of a relative gain measure and describes a new constructive induction algorithm (GALA) which is independent of the learning algorithm. Unlike most previous research on constructive induction, our methods are designed as preprocessing step before standard machine learning algorithms are applied. We present the results which demonstrate the effectiveness of GALA on artificial and real domains for several learners: C4.5, CN2, percept ron and backpropagation.