Machine Learning - Special issue on applications of machine learning and the knowledge discovery process
Using Correspondence Analysis to Combine Classifiers
Machine Learning
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
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Melanoma is the most dangerous skin cancer and early diagnosis is the main factor for its successful treatment. Experienced dermatologists with specific training make the diagnosis by clinical inspection and they reach 80% level of both sensitivity and specificity. In this paper, we present a multiclassifiers system for supporting the early diagnosis of melanoma. The system acquires a digital image of the skin lesion and extracts a set of geometric and colorimetric features. The diagnosis is performed on the vector of features by integrating with a voting schema the diagnostic outputs of three different classifiers: discriminant analysis, k-nearest neighbor and decision tree. The system is build and validated on a set of 152 skin images acquired via D-ELM. The results are comparable or better of the diagnostic response of a group of expert dermatologists.