TitleCombining two lazy learning methods for classification and knowledge discovery.
Publication TypeConference Paper
Year of Publication2011
AuthorsArmengol E, Puig S
Conference NameInternational Conference on Knowledge Discovery and Information Retrieval
PublisherINSTICC
Conference LocationSenart, Paris
Keywordsclassification, knowledge discovery, Lazy learning methods, Machine Learning, medical diagnosis
Abstract

The goal of this paper is to construct a classifier for diagnosing malignant melanoma. We experimented with two lazy learning methods, $k$-NN and \textsf{LID}, and compared their results with the ones produced by decision trees. We performed this comparison because we are also interested on building a domain model that can serve as basis to dermatologists to propose a good characterization of early melanomas. We shown that lazy learning methods have a better performance than decision trees in terms of sensitivity and specificity. We have seen that both lazy learning methods produce complementary results ($k$-NN has high specificity and LID has high sensitivity) suggesting that a combination of both could be a good classifier. We report experiments confirming this point. Concerning the construction of a domain model, we propose to use the explanations provided by the lazy learning methods, and we see that the resulting theory is as predictive and useful as the one obtained from decision trees.