Results 291 to 300 of about 1,671,237 (334)

From Ensemble of Fuzzy Classifiers to Single Fuzzy Rule Base Classifier

2008
Neuro-fuzzy systems show very good performance and the knowledge comprised within their structure is easily interpretable. To further improve their accuracy they can be combined into ensembles. In the paper we combine specially modified Mamdani neuro-fuzzy systems into an AdaBoost ensemble.
Marcin Korytkowski, Leszek Rutkowski, Rafał Scherer   +2 more
openaire   +1 more source

A Classifier Ensemble Method for Fuzzy Classifiers

2006
In this paper, a classifier ensemble method based on fuzzy integral for fuzzy classifiers is proposed. The object of this method is to reduce subjective factor in building a fuzzy classifier, and to improve the classification recognition rate and stability for classification system.
Ai-min Yang, Yong-mei Zhou, Min Tang
openaire   +1 more source

Fast neuro-fuzzy classifier

First International IEEE EMBS Conference on Neural Engineering, 2003. Conference Proceedings., 2004
The problem of data classification with the help of neuro-fuzzy and clustering techniques is considered. The architecture of a neuro-fuzzy classifier is proposed. It is characterized by the incorporation of possibilistic information into the consequents of classification rules.
Y.V. Gorshkov, I.V. Kokshenev, O.O. Rudnyeva   +2 more
openaire   +1 more source

Semantic Segmentation Based Crowd Tracking and Anomaly Detection via Neuro-fuzzy Classifier in Smart Surveillance System

The Arabian journal for science and engineering, 2022
F. Abdullah, Ahmad Jalal
semanticscholar   +1 more source

Fuzzy Classifier Systems

1993
Fuzzy classifier systems are genetic based machine learning systems which integrate a fuzzy rule base, a genetic algorithm and an apportionment of credit function. In this paper we present a Monte-Carlo selection rule which enables us to give a global convergence proof for (fuzzy) classifier systems and thus combines the advantages of genetic ...
openaire   +3 more sources

Neuro-Fuzzy Relational Classifiers

2004
In the paper, we present a new fuzzy relational system with multiple outputs for classification purposes. Rules in the system are more flexible than the rules in linguistic fuzzy systems because of the additional weights in rule consequents. The weights comes from an additional binary relation. Thanks to this, input and output fuzzy sets are related to
Rafał Scherer, Leszek Rutkowski
openaire   +1 more source

Robust fuzzy rough classifiers

Fuzzy Sets and Systems, 2011
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hu, Qinghua, An, Shuang, Yu, Xiao, Yu, Daren   +3 more
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Fuzzy neural classifier

2002
This chapter presents a special case of the fuzzy neural network introduced in Chapter 9, that is, a fuzzy neural classifier. Its learning and inference modes are discussed and its application in diagnosing surgical cases in the veterinary domain of equine colic is demonstrated.
openaire   +1 more source

Towards Incremental Fuzzy Classifiers

Soft Computing, 2006
Fuzzy classification systems (FCS) are traditionally built from observations (data points) in an off-line one shot-experiment. Once the learning phase is exhausted, the classifier is no more capable to learn further knowledge from new observations nor is it able to update itself in the future. This paper investigates the problem of incremental learning
Abdelhamid Bouchachia, Roland Mittermeir
openaire   +1 more source

Fuzzy Classifier Based on Fuzzy Decision Tree

EUROCON 2007 - The International Conference on "Computer as a Tool", 2007
A popular method for making a fuzzy decision tree for classification is Fuzzy ID3 algorithm. We introduce a new approach that uses cumulative information estimations of initial data. Based on these estimations we propose a new greedy version of fuzzy ID3 algorithm to be used to generate understandable fuzzy classification rules.
Vitaly Levashenko, Elena Zaitseva, Seppo Puuronen   +2 more
openaire   +1 more source