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Building a Lazy Domain Theory for Characterizing Malignant Melanoma
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Author(s): Eva Armengol (Artificial Intelligence Research Institute (IIIA-CSIC), Spain)and Susana Puig (Hospital Clínic i Provincial de Barcelona, Spain)
Copyright: 2012
Pages: 19
Source title:
Medical Applications of Intelligent Data Analysis: Research Advancements
Source Author(s)/Editor(s): Rafael Magdalena-Benedito (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Emilio Soria-Olivas (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Juan Guerrero Martínez (Intelligent Data Analysis Laboratory, University of Valencia, Spain), Juan Gómez-Sanchis (Intelligent Data Analysis Laboratory, University of Valencia, Spain)and Antonio Jose Serrano-López (Intelligent Data Analysis Laboratory, University of Valencia, Spain)
DOI: 10.4018/978-1-4666-1803-9.ch019
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Abstract
In this chapter, the authors propose an approach for building a model characterizing malignant melanomas. A common way to build a domain model is using an inductive learning method. Such resulting model is a generalization of the known examples. However, in some domains where there is not a clear difference among the classes, the inductive model could be too general. The approach taken in this chapter consists of using lazy learning methods for building what the authors call a lazy domain theory. The main difference between both inductive and lazy theories is that the former is complete whereas the latter is not. This means that the lazy domain theory may not cover all the space of known examples. The authors’ experiments have shown that, despite of this, the lazy domain theory has better performance than the inductive theory.
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