| Paper Details: | Downloads: 518 |
| Serial Number: | P1120535114
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| Title: | Extracting fuzzy classi cation rules with gene expression
programming
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| Authors: | M.H. Marghny and I.E. El-Semman
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| Abstract: | In essence, data mining consists of extracting knowledge
from data. This paper proposes an evolutionary
system for discovering fuzzy classi cation rules. Fuzzy
logic is useful for data mining especially in the case for
performing classi cation task. Three methods were
used to extract fuzzy classi cation rules using Evolutionary
Algorithms: (1) genetic selection small number
of large number of fuzzy candidate rules, (2) genetic
reduction of genetic space, selection fuzzy rules
from large the candidate rules, (3) genetic learning
of fuzzy classi cation rules. In this paper, we propose
a new gene expression programming (GEP) algorithm
for discovering logical fuzzy classi cation rules,
the proposed method has been tested and the results
are comparable with other techniques include Genetic
Programming (GP).
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| Keywords: | data mining, fuzzy classi cation rules,
logic operators, gene expression programming.
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| Journal/Conference: | ICGST Conference on Artificial Intelligence and Machine Learning, AIML-05
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| Volume: |
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| Issue: |
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| Submission Date: | 8/1/2005 12:00:00 AM
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| Review Date: | 10/1/2005 12:00:00 AM
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| Publishing Date: | 12/19/2005 12:00:00 AM
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| Article Downloads: | 518
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| Download: |
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