Search results for " set"
showing 10 items of 2095 documents
An integrated fuzzy cells-classifier
2007
This 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 improvement of the recognition rate using the genetic algorithm combination strategy compared with the recognition rate of each single classifier.
A genetic integrated fuzzy classifier
2005
This 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. The performance of our classifier is tested against a feed-forward neural network and a Support Vector Machine. Results show the good performance and robustness of the integrated classifier strategies.
Fuzzy Classifier Based on Fuzzy Decision Tree
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. The goal is to find a sequence of rules that causes near minimal classification costs.
Combining one class fuzzy KNN’s
2007
This paper introduces a parallel combination of N > 2 one class fuzzy KNN (FKNN) classifiers. The classifier combination consists of a new optimization procedure based on a genetic algorithm applied to FKNN’s, that differ in the kind of similarity used. We tested the integration techniques in the case of N = 5 similarities that have been recently introduced to face with categorical data sets. The assessment of the method has been carried out on two public data set, the Masquerading User Data (www.schonlau.net) and the badges database on the UCI Machine Learning Repository (http://www.ics.uci.edu/~mlearn/). Preliminary results show the better performance obtained by the fuzzy integration …
An Approach to the Concept of Soft Fuzzy Proximity
2014
The purpose of this paper is to introduce the concept of soft fuzzy proximity. Firstly, we give the definitions of soft fuzzy proximity and Katsaras soft fuzzy proximity, and also we investigate the relations between the soft fuzzy proximity and slightly modified version of Katsaras soft fuzzy proximity. Secondly, we induce a soft fuzzy topology from a given soft fuzzy proximity by using soft fuzzy closure operator. Then, we obtain the initial soft fuzzy proximity from a given family of soft fuzzy proximities. So, we describe products in the category of soft fuzzy proximities. Finally, we show that a family of all soft fuzzy proximities on a given set constitutes a complete lattice.
Different averages of a fuzzy set with an application to vessel segmentation
2005
Image segmentation is a major problem in image processing, particularly in medical image analysis. A great number of segmentation procedures produce intermediate gray-scale images that can be understood as fuzzy sets. Additionally, some segmentation procedures tend to leave free tuning parameters (very influential in the final binary image) for the user. These different binary images can be easily aggregated (into a fuzzy set) by making use of fuzzy set theory. In any case, a single binary image is required so our interest is to associate a crisp set to a given fuzzy set in an intelligent and unsupervised manner. The main idea of this paper is to define the averages of a given fuzzy set by …
Distance-constrained data clustering by combined k-means algorithms and opinion dynamics filters
2014
Data clustering algorithms represent mechanisms for partitioning huge arrays of multidimensional data into groups with small in–group and large out–group distances. Most of the existing algorithms fail when a lower bound for the distance among cluster centroids is specified, while this type of constraint can be of help in obtaining a better clustering. Traditional approaches require that the desired number of clusters are specified a priori, which requires either a subjective decision or global meta–information knowledge that is not easily obtainable. In this paper, an extension of the standard data clustering problem is addressed, including additional constraints on the cluster centroid di…
Comparison of Internal Clustering Validation Indices for Prototype-Based Clustering
2017
Clustering is an unsupervised machine learning and pattern recognition method. In general, in addition to revealing hidden groups of similar observations and clusters, their number needs to be determined. Internal clustering validation indices estimate this number without any external information. The purpose of this article is to evaluate, empirically, characteristics of a representative set of internal clustering validation indices with many datasets. The prototype-based clustering framework includes multiple, classical and robust, statistical estimates of cluster location so that the overall setting of the paper is novel. General observations on the quality of validation indices and on t…
Fuzzy Discrete Event Simulation for Fuzzy Production Systems Analysis
1998
Abstract Fuzzy production systems are characterised by vagueness in data and requirements that very often cannot be reduced to stochastic models. Therefore, such production systems cannot be analysed by using classical techniques such as Queue Theory or Discrete Simulation Analysis. On the other hand the great diffusion of Fuzzy Production environments in small and medium enterprises claims for the development of new analysis tools. This paper proposes a new approach to Discrete Event Simulation able to treat with fuzzy variables. A new methodology has been proposed to process fuzzy information within discrete event simulation and a prototype of a Fuzzy Discrete Event Simulator has been dev…
Decision Suport System for Manufacturing Processes Reengineering based upon Fuzzy Logic Techniques
2012
Abstract This work presents a method for taking the decision of reengineering a production system, based upon fuzzy techniques. The main advantage of this method is, after authors' opinion, is the ease of its implementation together with the reduced time for gathering data and processing it. Multi-variable decision systems are usually based upon complicated mathematical methods and involved a large amount of data to be processed. The fuzzy approach presented here is based only on five input variables and one output variable. The data for the model are gathered by simple queries and quizzes. Human perception, the main point of fuzzy logic, is widely used here for gathering input data for the…