Fuzzy relational classifier trained by fuzzy clustering

Pattern Recognition Letters, vol. 24, num. 14, pp. 2195-2207, 2003

The classical fuzzy classifier consists of rules each one describing one of the classes. In this paper a new fuzzy model structure is proposed where each rule can represent more than one classes with different probabilities. The obtained classifier can be considered as an extension of the quadratic Bayes classifier that utilizes mixture of models for estimating the class conditional densities. A supervised clustering algorithm has been worked out for the identification of this fuzzy model. The relevant input variables of the fuzzy classifier have been selected based on the analysis of the clusters by Fisher’s interclass separability criteria. This new approach is applied to the well-known wine and Wisconsin breast cancer classification problems.

Lecture Notes in Computer Science, 2004

In this paper we describe the implementation of a fuzzy relational neural network model. In the model, the input features are represented by fuzzy membership, the weights are described in terms of fuzzy relations. The output values are obtained with the max-min composition, and are given in terms of fuzzy class membership values. The learning algorithm is a modified version of back-propagation. The system is tested on an infant cry classification problem, in which the objective is to identify pathologies in recently born babies.

2008

In this article the issue of data based modeling is dealt with the help a network of uniform multivariate fuzzy classifiers. Within this framework the innovation consists in the specification of a hierarchical design strategy for such a network. Concretely, the two network specifying factors, namely the layout of the network structure and the classifier nodes configuration, will be addressed by a hierarchical clustering and selection strategy. The resulting network will represent the model's complexity in terms of interconnections between fuzzy classifier nodes whereas the model's vagueness and imprecision is captured within each fuzzy classifier node. Throughout the paper the network design and operation is illustrated with the help of an example.

In Pattern recognition, ensembles of classifiers are used to increase the performance and accuracy of classification systems. The creation of ensembles, selection of base classifiers and combining the decisions of the classifiers is an active research area. In this paper we propose a method of ensemble creation that is based on fuzzy clustering (Fuzzy C Mean) and fuzzy entropy; and named as Fuzzy Clustering and Fuzzy Entropy (FCFE) based classification model. With the help of FCM we obtained fuzzy membership matrix, revealing the underlying distribution and structure of the data. The Fuzzy entropy tells us about the degree of difficulty of classification of data. This information is used in sampling the training data into core sample and boundary sample. This sampling approach induces diversity in the ensemble which contributes to higher classification accuracy. The proposed method is evaluated on 4 UCI benchmark data sets with support vector machine (SVM) as the base classifier. The decision is combined using mean combiner rule. The results show that the proposed method delivers higher classification accuracy than stand alone SVM and the well known ensembles techniques of Bagging and Boosting.

Springer eBooks, 2005

Preface vii 1 Pattern Recognition 1 1.1 Fuzzy models for pattern recognition 1 1.2 Why fuzzy pattern recognition? 1.3 Overview of the volume 8 1.4 Comments and bibliography 2 Cluster Analysis for Object Data 2.1 Cluster analysis 2.2 Batch point-prototype clustering models A. The c-means models B. Semi-supervised clustering models 23 C. Probabilistic Clustering D. Remarks on HCM/FCM/PCM E. The Reformulation Theorem 2.3 Non point-prototype clustering models A. The Gustafson-Kessel (GK) Model B. Linear manifolds as prototypes C. Spherical Prototypes D. Elliptical Prototypes E. Quadric Prototypes F. Norm induced shell prototypes G. Regression models as prototypes H. Clustering for robust parametric estimation 2.4 Cluster Validity A. Direct Measures B. Davies-Bouldin Index C. Dunn's index D. Indirect measures for fuzzy clusters E. Standardizing and normalizing indirect indices F. Indirect measures for non-point prototype models G. Fuzzification of statistical indices 2.5 Feature Analysis 2.6 Comments and bibliography vi FUZZY PATTERN RECOGNITION 3 Cluster Analysis for Relational Data 3.1 Relational Data A. Crisp Relations B. Fuzzy Relations 3.2 Object Data to Relational Data 3.3 Hierarchical Methods 3.4 Clustering by decomposition of fuzzy relations 3.5 Relational clustering with objective functions A. The Fuzzy Non Metric (FNM) model B. The Assignment-Prototype (AP) Model C. The relational fuzzy c-means (RFCM) model D. The non-Euclidean RFCM (NERFCM) model 3.6 Cluster validity for relational models 3.7 Comments and bibliography 4 Classifier Design 4.1 Classifier design for object data 4.2 Prototype classifiers A. The nearest prototype classifier B. Multiple prototype designs 4.3 Methods of prototype generation A. Competitive learning networks B. Prototype relabeling C. Sequential hard c-means (SHCM) D. Learning vector quantization (LVQ) E. Some soft versions of LVQ F. Case Study : LVQ and GLVQ-F 1-nmp designs G. The soft competition scheme (SCS) H. Fuzzy learning vector quantization (FLVQ) 1. The relationship between c-Means and CL schemes J. The mountain "clustering" method (MCM) 4.4 Nearest neighbor classifiers 4.5 The Fuzzy Integral 4.6 Fuzzy Rule-Based Classifiers A. Crisp decision trees B. Rules from crisp decision trees C. Crisp decision tree design D. Fuzzy system models and function approximation E. The Chang -Pavlidis fuzzy decision tree F. Fuzzy relatives of 1D3 G. Rule-based approximation based on clustering H. Heuristic rule extraction I. Generation of fuzzy labels for training data 4.7 Neural-like architectures for classification A. Biological and mathematical neuron models B. Neural network models C. Fuzzy Neurons D. Fuzzy aggregation networks E. Rule extraction with fuzzy aggregation networks Contents vii 4.8 Adaptive resonance models A. The ARTl algorithm B. Fuzzy relatives of ART C. Radial basis function networks 4.9 Fusion techniques A. Data level fusion B. Feature level fusion C. Classifier fusion 4.10 Syntactic pattern recognition A. Language-based methods B. Relation-based methods 4.11 Comments and bibliography 5 Image Processing and Computer 'Vision 5.