Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Classifier systems and genetic algorithms
Machine learning: paradigms and methods
A new version of the rule induction system LERS
Fundamenta Informaticae
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Postprocessing of Rule Sets Induced from a Melanoma Data Set
COMPSAC '02 Proceedings of the 26th International Computer Software and Applications Conference on Prolonging Software Life: Development and Redevelopment
A Comparison of Six Discretization Algorithms Used for Prediction of Melanoma
Proceedings of the IIS'2002 Symposium on Intelligent Information Systems
Melanoma Prediction Using Data Mining System LERS
COMPSAC '01 Proceedings of the 25th International Computer Software and Applications Conference on Invigorating Software Development
Diagnosis of melanoma based on data mining and ABCD formulas
Design and application of hybrid intelligent systems
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Our main objective was to decrease the error rate of diagnosis of melanoma, a very dangerous skin cancer. Since diagnosticians routinely use the so-called ABCD formula for melanoma prediction, our main concern was to improve the ABCD formula. In our search for the best coefficients of the ABCD formula we used two different discretization methods, agglomerative and divisive, both based on cluster analysis. In our experiments we used the data mining system LERS (Learning from Examples based on Rough Sets). As a result of more than 30,000 experiments, two optimal ABCD formulas were found, one with the use of the agglomerative method, the other one with divisive. These formulas were evaluated using statistical methods. Our final conclusion is that it is more important to use an appropriate discretization method than to modify the ABCD formula. Also, the divisive method of discretization is better than agglomerative. Finally, diagnosis of melanoma without taking into account results of the ABCD formula is much worse, i.e., the error rate is significantly greater, comparing with any form of the ABCD formula.