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An integrated fuzzy cells-classifier
Image and Vision Computing, 2006This paper introduces a genetic algorithm able to combine different classifiers based on different distance functions. The use of a genetic algorithm is motivated by the fact that the combination phase is based on the optimization of a vote strategy. The method has been applied to the classification of four types of biological cells, results show an ...
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A genetic integrated fuzzy classifier
Pattern Recognition Letters, 2005This paper introduces a new classifier, that is based on fuzzy-integration schemes controlled by a genetic optimisation procedure. Two different types of integration are proposed here, and are validated by experiments on real data sets of biological cells.
DI GESU', Vito, LO BOSCO, Giosue'
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2009
In this chapter, we introduce three design strategies of classifiers which exploit the unified usage of the AFS fuzzy logic, entropy measures and decision trees. The advantage of these classifiers is two-fold. First, they can mimic the human reasoning and in this manner offer a far more transparent and comprehensible way supporting the design of the ...
Xiaodong Liu, Witold Pedrycz
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In this chapter, we introduce three design strategies of classifiers which exploit the unified usage of the AFS fuzzy logic, entropy measures and decision trees. The advantage of these classifiers is two-fold. First, they can mimic the human reasoning and in this manner offer a far more transparent and comprehensible way supporting the design of the ...
Xiaodong Liu, Witold Pedrycz
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Expert systems with applications, 2019
Objective: In this study we propose a fuzzy classifier whose rule antecedents are determined based on the new approach to Clustering with Pairs of Prototypes (CPP).
M. Jezewski +3 more
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Objective: In this study we propose a fuzzy classifier whose rule antecedents are determined based on the new approach to Clustering with Pairs of Prototypes (CPP).
M. Jezewski +3 more
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Intrusion detection system based on GA‐fuzzy classifier for detecting malicious attacks
Concurrency and Computation, 2019Usage of computer resources, being a very important part in day to day life, it is to be noticed that the security threats have also increased. Hence, Intrusion Detection System (IDS) is used for detection and prevention of computer resources from ...
K. Pradeep +4 more
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Fuzzy classifiers versus cost-based Bayes classifiers
NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society, 2006Learning classifiers for imbalanced data sets is a difficult task for current machine learning algorithms. The difficulty can be traced to the fact that being accuracy driven, most algorithms lead to classifiers which are biased towards the majority class.
Anca Ralescu, Sofia Visa
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Design of deep ensemble classifier with fuzzy decision method for biomedical image classification
Applied Soft Computing, 2021Research on biomedical science has many components like biomedical engineering, biomedical signal processing, gene analysis, and biomedical image processing. Classification, detection, and recognition have a great value for disease diagnosis and analysis.
Abhishek Das +2 more
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Design of Reinforced Interval Type-2 Fuzzy C-Means-Based Fuzzy Classifier
IEEE transactions on fuzzy systems, 2018This paper is concerned with a new design methodology of a reinforced interval type-2 fuzzy c-means (FCM) based fuzzy classifier (FC). The key point of this study is to reduce the computational complexity of type-2 fuzzy set-based models and to alleviate
Eun-Hu Kim, Sung-Kwun Oh, W. Pedrycz
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Fuzzy Multi-Instance Classifiers
IEEE Transactions on Fuzzy Systems, 2016Multi-instance learning is a setting in supervised learning where the data consist of bags of instances. Samples in the dataset are groups of individual instances. In classification problems, a decision value is assigned to the entire bag, and the classification of an unseen bag involves the prediction of the decision value based on the instances it ...
Sarah Vluymans +4 more
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Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063), 2002
Several ways of combining concepts of fuzzy set theory with connectionist methods are known. We focus on the use of fuzzy numbers in neural networks. Our goal is to create a fully fuzzified Kohonen-layer which receives fuzzy numbers as inputs and computes its output employing fuzzy weights. The main problem is the determination of the winning neuron by
U. Sprekelmeyer +2 more
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Several ways of combining concepts of fuzzy set theory with connectionist methods are known. We focus on the use of fuzzy numbers in neural networks. Our goal is to create a fully fuzzified Kohonen-layer which receives fuzzy numbers as inputs and computes its output employing fuzzy weights. The main problem is the determination of the winning neuron by
U. Sprekelmeyer +2 more
openaire +1 more source