Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
C4.5: programs for machine learning
C4.5: programs for machine learning
Foundations of genetic programming
Foundations of genetic programming
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Genetic Programming with a Genetic Algorithm for Feature Construction and Selection
Genetic Programming and Evolvable Machines
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Classifier design with feature selection and feature extraction using layered genetic programming
Expert Systems with Applications: An International Journal
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Genetic programming for simultaneous feature selection and classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Genetic Programming for Feature Subset Ranking in Binary Classification Problems
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Genetic programming for biomarker detection in mass spectrometry data
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
A genetic programming approach to hyper-heuristic feature selection
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Feature ranking (FR) provides a measure of usefulness for the attributes of a classification task. Most existing FR methods focus on the relevance of a single feature to the class labels. Here, we use GP to see how a set of features can contribute towards discriminating different classes and then we score the participating features accordingly. The scoring mechanism is based on the frequency of appearance of each feature in a collection of GP programs and the fitness of those programs. Our results show that the proposed FR method can detect important features of a problem. A variety of different classifiers restricted to just a few of these high-ranked features work well. The ranking mechanism can also shrink the search space of size O (2 n ) of subsets of features to a search space of size O (n ) in which there are points that may improve the classification performance.