A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Nearest neighbor classifier: simultaneous editing and feature selection
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Machine Learning
Text Categorization Based on Regularized Linear Classification Methods
Information Retrieval
A Representation for the Adaptive Generation of Simple Sequential Programs
Proceedings of the 1st International Conference on Genetic Algorithms
New Methods for Splice Site Recognition
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Genetic Programming for Mining DNA Chip Data from Cancer Patients
Genetic Programming and Evolvable Machines
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A hybrid genetic algorithm for feature selection wrapper based on mutual information
Pattern Recognition Letters
New methods for competitive coevolution
Evolutionary Computation
Shuffling biological sequences with motif constraints
Journal of Discrete Algorithms
Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Genetic programming for simultaneous feature selection and classifier design
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fast generic selection of features for neural network classifiers
IEEE Transactions on Neural Networks
Binary Response Models for Recognition of Antimicrobial Peptides
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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Associating functional information with biological sequences remains a challenge for machine learning methods. The performance of these methods often depends on deriving predictive features from the sequences sought to be classified. Feature generation is a difficult problem, as the connection between the sequence features and the sought property is not known a priori. It is often the task of domain experts or exhaustive feature enumeration techniques to generate a few features whose predictive power is then tested in the context of classification. This paper proposes an evolutionary algorithm to effectively explore a large feature space and generate predictive features from sequence data. The effectiveness of the algorithm is demonstrated on an important component of the gene-finding problem, DNA splice site prediction. This application is chosen due to the complexity of the features needed to obtain high classification accuracy and precision. Our results test the effectiveness of the obtained features in the context of classification by Support Vector Machines and show significant improvement in accuracy and precision over state-of-the-art approaches.