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
Genetic programming II: automatic discovery of reusable programs
Genetic programming II: automatic discovery of reusable programs
Recombination, selection, and the genetic construction of computer programs
Recombination, selection, and the genetic construction of computer programs
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Information-theoretic algorithm for feature selection
Pattern Recognition Letters
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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
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
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
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
Multi-objective genetic algorithm evaluation in feature selection
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
A multi-objective feature selection approach based on binary PSO and rough set theory
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
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In this paper we use genetic programming (GP) for feature selection in binary classification tasks. Mathematical expressions built by GP transform the feature space in a way that the relevance of subsets of features can be measured using a simple relevance function. We make some modifications to the standard GP to make it explore large subsets of features when necessary. This is done by increasing the depth limit at run-time and at the same time trying to avoid bloating and overfitting by some control mechanism. We take a filter (non-wrapper) approach to exploring the search space. Unlike most filter methods that usually deal with single features, we explore subsets of features. The solution of the proposed search is a vector of Pareto-front points. Our experiments show that a linear search over this vector can improve the classification performance of classifiers while decreasing their complexity.