Authors:
Eva Armengol
1
and
Susana Puig
2
Affiliations:
1
Artificial Intelligence Research Institute (IIIA-CSIC), Spain
;
2
Hospital Clínic i Provincial de Barcelona, Spain
Keyword(s):
Machine learning, Lazy learning methods, Knowledge discovery, Classification, Medical diagnosis.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
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 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.