Combining evolutionary and fuzzy techniques in medical diagnosis

Carlos Andrés Pena-Reyes; Moshe Sipper

doi:10.1007/978-3-7908-1788-1_14

Combining evolutionary and fuzzy techniques in medical diagnosis

Carlos Andrés Pena-Reyes, Moshe Sipper

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-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 language	English
Title of host publication	Computational Intelligence Processing in Medical Diagnosis
Editors	M. Schmitt, H.-N. Teodorescu, A. Jain, S. Jain
Publisher	Physica Heidelberg
Chapter	14
Pages	391-426
Number of pages	36
ISBN (Electronic)	9783790817881
ISBN (Print)	9783790825091, 9783790814637
DOIs	https://doi.org/10.1007/978-3-7908-1788-1_14
State	Published - Mar 2002

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10.1007/978-3-7908-1788-1_14

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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.",

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AB - 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.

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DO - 10.1007/978-3-7908-1788-1_14

M3 - Chapter

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SN - 9783790814637

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BT - Computational Intelligence Processing in Medical Diagnosis

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A2 - Jain, S.

PB - Physica Heidelberg

ER -

Combining evolutionary and fuzzy techniques in medical diagnosis

Abstract

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