%0 Conference Paper %1 Winkler:2006:GECCOWKS %A Winkler, Stephan M. %A Affenzeller, Michael %A Wagner, Stefan %B MedGEC 2006 GECCO Workshop on Medical Applications of Genetic and Evolutionary Computation %C Seattle, WA, USA %D 2006 %E Smith, Stephen L %E Cagnoni, Stefano %E van Hemert, Jano %K Adaptation/Self-Adaptation, Classifier Empirical Medicine Study, Systems, algorithms, genetic programming. %T Using Enhanced Genetic Programming Techniques for Evolving Classifiers in the Context of Medical Diagnosis - An Empirical Study %U http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/wksp115.pdf %X There are several data based methods in the field of artificial intelligence which are nowadays frequently used for analysing classification problems in the context of medical applications. As we show in this paper, the application of enhanced evolutionary computation techniques to classification problems has the potential to evolve classifiers of even higher quality than those trained by standard machine learning methods. On the basis of three medical benchmark classification problems, namely the Wisconsin and the Thyroid data sets taken from the UCI repository as well as the Melanoma data set prepared by members of the Department of Dermatology of the Medical University Vienna, we document that the enhanced genetic programming based approach presented here is able to produce better results than linear modelling methods, artificial neural networks, kNN classification and also standard genetic programming approaches.

@inproceedings{Winkler:2006:GECCOWKS, abstract = {There are several data based methods in the field of artificial intelligence which are nowadays frequently used for analysing classification problems in the context of medical applications. As we show in this paper, the application of enhanced evolutionary computation techniques to classification problems has the potential to evolve classifiers of even higher quality than those trained by standard machine learning methods. On the basis of three medical benchmark classification problems, namely the Wisconsin and the Thyroid data sets taken from the UCI repository as well as the Melanoma data set prepared by members of the Department of Dermatology of the Medical University Vienna, we document that the enhanced genetic programming based approach presented here is able to produce better results than linear modelling methods, artificial neural networks, kNN classification and also standard genetic programming approaches.}, added-at = {2008-06-19T17:35:00.000+0200}, address = {Seattle, WA, USA}, author = {Winkler, Stephan M. and Affenzeller, Michael and Wagner, Stefan}, biburl = {https://www.bibsonomy.org/bibtex/2082607ddcc1ec30a768feef7d6b7255b/brazovayeye}, booktitle = {MedGEC 2006 GECCO Workshop on Medical Applications of Genetic and Evolutionary Computation}, editor = {Smith, Stephen L and Cagnoni, Stefano and {van Hemert}, Jano}, interhash = {4615065104ff44f6355388a35faba496}, intrahash = {082607ddcc1ec30a768feef7d6b7255b}, keywords = {Adaptation/Self-Adaptation, Classifier Empirical Medicine Study, Systems, algorithms, genetic programming.}, month = {8 July}, notes = {GECCO-2006WKS Distributed on CD-ROM at the GECCO 2006 conference}, size = {8 pages}, timestamp = {2008-06-19T17:54:16.000+0200}, title = {Using Enhanced Genetic Programming Techniques for Evolving Classifiers in the Context of Medical Diagnosis - An Empirical Study}, url = {http://www.cs.bham.ac.uk/~wbl/biblio/gecco2006etc/papers/wksp115.pdf}, year = 2006 }