A methodology for automated fuzzy model generation
| dc.contributor.author | Tsipouras, M. G. | en |
| dc.contributor.author | Exarchos, T. P. | en |
| dc.contributor.author | Fotiadis, D. I. | en |
| dc.date.accessioned | 2015-11-24T17:33:31Z | |
| dc.date.available | 2015-11-24T17:33:31Z | |
| dc.identifier.issn | 0165-0114 | - |
| dc.identifier.uri | https://olympias.lib.uoi.gr/jspui/handle/123456789/13870 | |
| dc.rights | Default Licence | - |
| dc.subject | decision trees | en |
| dc.subject | fuzzy modeling | en |
| dc.subject | optimization | en |
| dc.subject | weighted fuzzy rules | en |
| dc.subject | inductive learning-method | en |
| dc.subject | decision trees | en |
| dc.subject | classification | en |
| dc.subject | framework | en |
| dc.subject | optimization | en |
| dc.subject | accuracy | en |
| dc.subject | creation | en |
| dc.subject | systems | en |
| dc.subject | rules | en |
| dc.title | A methodology for automated fuzzy model generation | en |
| heal.abstract | In this paper we propose a generic methodology for the automated generation of fuzzy models. The methodology is realized in three stages. Initially, a crisp model is created and in the second stage it is transformed to a fuzzy one. In the third stage, all parameters entering the fuzzy model are optimized. The proposed methodology is novel and generic since it can integrate alternative techniques in each of its stages. A specific realization of this methodology is implemented, using decision trees for the creation of the crisp model, the sigmoid function, the min-max operators and the maximum defuzzifier, for the transformation of the crisp model into a fuzzy one, and four different optimization strategies, including global and local optimization techniques, as well as. hybrid approaches. The proposed methodology presents several advantages and novelties: the transformation of the crisp model to the respective fuzzy one is straightforward ensuring its full automated nature and it introduces a set of parameters, expressing the importance of each fuzzy rule. The above realization is extensively evaluated using several benchmark data sets front the UCI machine learning repository and the obtained results indicate its high efficiency. (C) 2008 Elsevier B.V. All rights reserved. | en |
| heal.access | campus | - |
| heal.fullTextAvailability | TRUE | - |
| heal.identifier.primary | DOI 10.1016/j.fss.2008.04.004 | - |
| heal.identifier.secondary | <Go to ISI>://000260713000005 | - |
| heal.identifier.secondary | http://ac.els-cdn.com/S0165011408002212/1-s2.0-S0165011408002212-main.pdf?_tid=eb4939ad420bc07676e78a4d1e9c9a1d&acdnat=1339758721_bf2a088e25745dba00fa8098db7ba042 | - |
| heal.journalName | Fuzzy Sets and Systems | en |
| heal.journalType | peer reviewed | - |
| heal.language | en | - |
| heal.publicationDate | 2008 | - |
| heal.publisher | Elsevier | en |
| heal.recordProvider | Πανεπιστήμιο Ιωαννίνων. Σχολή Θετικών Επιστημών. Τμήμα Μηχανικών Επιστήμης Υλικών | el |
| heal.type | journalArticle | - |
| heal.type.el | Άρθρο Περιοδικού | el |
| heal.type.en | Journal article | en |
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