The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning for the Detection of Oil Spills in Satellite Radar Images
Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Inference for the Generalization Error
Machine Learning
Cost-sensitive boosting for classification of imbalanced data
Pattern Recognition
Translation initiation site prediction on a genomic scale
Bioinformatics
Class imbalance methods for translation initiation site recognition
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
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Translation initiation site (TIS) recognition is one of the first steps in gene structure prediction, and one of the common components in any gene recognition system. Many methods have been described in the literature to identify TIS in transcribed sequences such as mRNA, EST and cDNA sequences. However, the recognition of TIS in DNA sequences is a far more challenging task, and the methods described so far for transcripts achieve poor results in DNA sequences. In this work we present the application of response surfaces to the problem of TIS recognition. Response surfaces are a powerful tool for both classification and regression as they are able to model many different phenomena and construct complex boundaries between classes. Furthermore, the interpretability of the results is very interesting from the point of view of the expert. In this paper we show the use of real-coded genetic algorithms for evolving a response surface that learns to classify TIS. The results obtained in three different organisms are comparable with a well-known classification algorithm with a more interpretable polynomial function.