Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Genetic programming: an introduction: on the automatic evolution of computer programs and its applications
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Feature Selection via Analysis of Relevance and Redundancy
The Journal of Machine Learning Research
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
Proceedings of the 9th annual conference on Genetic and evolutionary computation
A Direct Method of Nonparametric Measurement Selection
IEEE Transactions on Computers
Automated discovery of local search heuristics for satisfiability testing
Evolutionary Computation
Genetic Programming for Feature Ranking in Classification Problems
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Genetic Programming for Feature Subset Ranking in Binary Classification Problems
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Learning with many irrelevant features
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A genetic programming based hyper-heuristic approach for combinatorial optimisation
Proceedings of the 13th annual conference on Genetic and evolutionary computation
On the effectiveness of receptors in recognition systems
IEEE Transactions on Information Theory
An evolutionary-based hyper-heuristic approach for the Jawbreaker puzzle
Applied Intelligence
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Feature selection is the task of finding a subset of original features which is as small as possible yet still sufficiently describes the target concepts. Feature selection has been approached through both heuristic and meta-heuristic approaches. Hyper-heuristics are search methods for choosing or generating heuristics or components of heuristics, to solve a range of optimisation problems. This paper proposes a genetic-programming-based hyper-heuristic approach to feature selection. The proposed method evolves new heuristics using some basic components (building blocks). The evolved heuristics act as new search algorithms that can search the space of subsets of features. The classification performance (accuracy) of classifiers are improved by using small subsets of features found by evolved heuristics.