Combining evolutionary and fuzzy techniques in medical diagnosis

Carlos Andrés Pena-Reyes, Moshe Sipper

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

In this chapter we focus on the Wisconsin breast cancer diagnosis (WBCD) problem, combining two methodologies—fuzzy systems and evolutionary algorithms—to automatically produce diagnostic systems. We present two hybrid approaches: (1) a fuzzy-genetic algorithm, and (2) Fuzzy CoCo, a novel cooperative coevolutionary approach to fuzzy modeling. Both methods produce systems exhibiting high classification performance, and which are also human-interpretable. Fuzzy CoCo obtains higher-performance systems than the standard fuzzy-genetic approach while using less computational effort.
Original languageEnglish
Title of host publicationComputational Intelligence Processing in Medical Diagnosis
EditorsM. Schmitt, H.-N. Teodorescu, A. Jain, S. Jain
Place of PublicationHeidelberg
PublisherPhysica-Verlag
Chapter14
Pages391-426
Number of pages36
Volume96
ISBN (Print)978-3-7908-2509-1
DOIs
StatePublished - 2002

Fingerprint

Dive into the research topics of 'Combining evolutionary and fuzzy techniques in medical diagnosis'. Together they form a unique fingerprint.

Cite this