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About 10,000 papers for “Fuzzy classification”

Fuzzy Classification Aggregation

Federico Fioravanti · arXiv (Cornell University) · 2024

We consider the problem where a set of individuals has to classify $m$ objects into $p$ categories and does so by aggregating the individual classifications. We show that if $m\geq 3$, $m\geq p\geq 2$, and classifications are fuzzy, that is, objects belong to a category to a certain degree, then an optimal and independent aggregator rule that satisfies a weak unanimity condition belongs to the family of Weighted Arithmetic Means. We also obtain characterization results for $m= p= 2$.

Cited by 0Open access

Fuzzy classification analysis.

Kausar Noreen (21410391), Tahir Azeem (21410394), Faisal Rehman (5664848) et al. · Figshare · 2025

<b>(A)</b> Density plot depicting the membership function distribution for the fuzzy classification reflecting the level of association of data points with each cluster. The overlapping areas indicate shared characteristics or transitional zones between clusters. <b>(B)</b> Ambiguity histogram for fuzzy classification results depicting ambiguity in the classification of the clusters. The low ambiguity score for most data points suggests a clear cluster assignment.

Cited by 0Open access

Fuzzy hyperbox classification

Bargiela, A · Nottingham Trent University's Institutional Repository (Nottingham Trent Repository) · 2003

Cited by 0

Fuzzy classification systems

A. del Amo, Javier Montero, Gregory S. Biging et al. · European Journal of Operational Research · 2003

Cited by 137

For Fuzzy Classification of Databases with Fuzzy Classification Query Language

Seyfali Mahini · Digital Technologies Research and Applications · 2022

Business information systems have extensive databases that are mainly managed in relational databases. What is often missing are automated procedures to analyze these inventories without major restructuring. Based on this, we develop the Fuzzy Classification Query Language, FCQL, which enables fuzzy queries to the extended database schema using linguistic variables and converts them into SQL statements to the database. With this, we give the user a data mining tool so that he can start extended queries on his databases based on a pre-defined fuzzy classification and obtain an improved basis for decision making. As a result, the fuzzy classification query language enables marketers to improve customer value, launch useful programs, automate overall customization, and refine business campaigns.

Cited by 0Open access

Neuro-Fuzzy Classification Initialized By Fuzzy Clustering

Detlef Nauck, Frank Klawonn · 1996

In this paper we discuss how a neuro--fuzzy classifier can be initialized by rules generated by fuzzy clustering. The neuro--fuzzy classifier NEFCLASS can learn fuzzy classification rules completely from data. The learning algorithm for fuzzy sets can be constrained in order to obtain interpretable classifiers. However, fuzzy clustering provides more sophisticated rule learning procedures. We show that the learning process of NEFCLASS produces better results, if it is initialized by fuzzy clustering. 1 Introduction The determination of fuzzy rules from data is currently a growing research topic. Fuzzy rules are used e.g. to build fuzzy controllers, fuzzy classifiers, or they are needed to support decision making processes. Classification and decision support with the help of fuzzy rules is the background for this paper. In a lot of commercial areas there are huge unstructured data collections (e.g. banking, insurance, medical treatment etc.), and there is a need to transfer data into ...

Cited by 0

Fuzzy Classification: An Overview

Klaus Dieter Meyer Gramann · 1994

Fuzzy classification, fuzzy diagnosis, and fuzzy data analysis are — besides fuzzy control — the most important application areas of fuzzy logic. In this chapter four practical tasks are presented which can roughly be characterized as technical classification and diagnosis, fuzzy data analysis in chemical model creation, medical object recognition, and decision making support by a life insurance. Neural networks and analytical methods of classical statistics try to find explicitely a classifying function with the help of a sample. The development of knowledge-based systems has stimulated modern constructive approaches like IF-THEN-rules and causal networks. In order to deal with vague observations, vague relationships between features, and/or non-crip classification, these analytical and constructive methods were transfered from crisp numbers to fuzzy sets. The contributions of fuzzy sets to the four applications of this chapter are presented. Some general remarks on the applicability and limitations of fuzzy classification conclude this short introduction to fuzzy classification.

Cited by 4

Fuzzy Classification of Spectra

Theo E. Schouten, Maurice S. Klein Gebbinck · 1997

. Measured spectra are often a composition of a number of basic spectra. The purpose of fuzzy classification is to determine the presence and abundance of the basic spectra in a measured spectrum. In this paper spectra from remote sensing imaging spectrometers with 6 resp. 220 spectral bands are considered each composed of 3 basic spectra from a set of 6 resp. 11 basic spectra. It will be shown that a backpropagation neural network is able to determine the types and abundances of the basic spectra in a mixed spectrum. 1 Introduction Measured spectra are often a composition of a number of basic spectra. In remote sensing, imaging spectrometers [1] acquire images simultaneously in many narrow spectral bands from visible to infrared. Due to the limited spatial resolution, a pixel often contains more than one ground cover e.g. grass, mais and a water filled ditch in between. The purpose of fuzzy classification or mixed pixel analysis is to determine the fractions of the ground cover categ...

Cited by 0

Classification with fuzzy hypermatrices

Annamária R. Várkonyi-Kóczy, Balázs Tusor, János T. Tóth · 2016

This paper presents a novel classification method designed specifically for fast evaluation of problems with relatively low attribute quantities, such as image processing problems. The idea lies in the pre-calculation of fuzzy membership functions and their storage in an easily implementable structure. During the evaluation step the stored values can be accessed easily and quickly, making the processing of large quantities of data (like the pixels of images) significantly faster. A training algorithm is proposed and experimental results are shown to demonstrate the efficiency of the proposed technique.

Cited by 2