Search results for "Pattern recognition"
showing 10 items of 2301 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.
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 …
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 …
Unsupervised tissue classification of brain MR images for voxel-based morphometry analysis
2016
In this article, a fully unsupervised method for brain tissue segmentation of T1-weighted MRI 3D volumes is proposed. The method uses the Fuzzy C-Means (FCM) clustering algorithm and a Fully Connected Cascade Neural Network (FCCNN) classifier. Traditional manual segmentation methods require neuro-radiological expertise and significant time while semiautomatic methods depend on parameter's setup and trial-and-error methodologies that may lead to high intraoperator/interoperator variability. The proposed method selects the most useful MRI data according to FCM fuzziness values and trains the FCCNN to learn to classify brain’ tissues into White Matter, Gray Matter, and Cerebro-Spinal Fluid in …
Paradigm of tunable clustering using Binarization of Consensus Partition Matrices (Bi-CoPaM) for gene discovery
2013
Copyright @ 2013 Abu-Jamous et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Clustering analysis has a growing role in the study of co-expressed genes for gene discovery. Conventional binary and fuzzy clustering do not embrace the biological reality that some genes may be irrelevant for a problem and not be assigned to a cluster, while other genes may participate in several biological functions and should simultaneously belong to multiple clusters. Also, these algorithms cannot generate tight cluster…
Gravitational weighted fuzzy c-means with application on multispectral image segmentation
2014
This paper presents a novel clustering approach based on the classic Fuzzy c-means algorithm. The approach is inspired from the concept of interaction between objects in physics. Each data point is regarded as a particle. A specific weight is associated with each data particle depending on its interaction with other particles. This interaction is induced by attraction forces between pairs of particles and the escape velocity from other particles. Classification experiments using two data sets from UCI repository demonstrate the outperformance of the proposed approach over other clustering algorithms. In addition, results demonstrate the effectiveness of the proposed scheme for segmentation …
Semi-automated and interactive segmentation of contrast-enhancing masses on breast DCE-MRI using spatial fuzzy clustering
2022
Abstract Multiparametric Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer detection and is increasingly playing a key role in lesion characterization. In this context, accurate and reliable quantification of the shape and extent of breast cancer is crucial in clinical research environments. Since conventional lesion delineation procedures are still mostly manual, automated segmentation approaches can improve this time-consuming and operator-dependent task by annotating the regions of interest in a reproducible manner. In this work, a semi-automated and interactive approach based on the spatial Fuzzy C-Means (sFCM) algorithm is proposed, used to segme…
Accurate detection and characterization of corner points using circular statistics and fuzzy clustering
1998
Accurate detection and characterization of corner points in grey level images is considered as a pattern recognition problem. The method considers circular statistic tests to detect 2D features. A fuzzy clustering algorithm is applied to the edge orientations near the prospective corners to detect and classify them. The method is based on formulating hypotheses about the distribution of these orientations around an edge, corner or other 2-D feature. The method may provide accurate estimates of the direction of the edges that converge in a corner, along with their confidence intervals. Experimental results show the method to be robust enough against noise and contrast changes. Fuzzy membersh…
A Novel Clustering Algorithm based on a Non-parametric "Anti-Bayesian" Paradigm
2015
The problem of clustering, or unsupervised classification, has been solved by a myriad of techniques, all of which depend, either directly or implicitly, on the Bayesian principle of optimal classification. To be more specific, within a Bayesian paradigm, if one is to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the distance from the corresponding means or central points in the respective distributions. When this principle is applied in clustering, one would assign an unassigned sample into the cluster whose mean is the closest, and this can be done in either a bottom-up or a top-dow…