C4.5: programs for machine learning
C4.5: programs for machine learning
Learning Decision Trees Using the Area Under the ROC Curve
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Evolving Receiver Operating Characteristics for Data Fusion
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
The Journal of Machine Learning Research
A new ant colony algorithm for multi-label classification with applications in bioinfomatics
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Intelligent fusion of evidence from multiple sources for text classification
Intelligent fusion of evidence from multiple sources for text classification
Iterative filter generation using genetic programming
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Evolving rules for document classification
EuroGP'05 Proceedings of the 8th European conference on Genetic Programming
An evolutionary approach for motif discovery and transmembrane protein classification
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Semi-supervised genetic programming for classification
Proceedings of the 13th annual conference on Genetic and evolutionary computation
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Data Mining and Knowledge Discovery
A neuro-fuzzy immune inspired classifier for task-oriented texts
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Since the genomics revolution, bioinformatics has never been so popular. Many researchers have investigated with great success the use of evolutionary computation in bioinformatics [19] for example in the field of protein folding or determining genome sequences. In this paper, instead of using evolutionary computation as a way to provide new and innovative solutions to complex bioinformatics problems, we use genetic programming as a tool to evolve programs that are able to automatically classify research papers as dealing or not with a given protein. In a second part, we show that the attributes that are selected by the genetic programming evolved programs can be used efficiently for proteins classification.