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A Kernel Fuzzy c-Means Clustering-Based Fuzzy Support Vector Machine Algorithm for Classification Problems With Outliers or Noises

Xiaowei Yang, Guangquan Zhang, Jie Lu, Jun Ma
2011 IEEE transactions on fuzzy systems  
The results indicate that the KFCM-FSVM algorithm is robust for classification problems with outliers or noises.  ...  The results indicate that the KFCM-FSVM algorithm is robust for classification problems with outliers or noises.  ...  A reliable validation index for a fuzzy clustering must consider both the compactness and the separation of the fuzzy partition.  ... 
doi:10.1109/tfuzz.2010.2087382 fatcat:2z2ukuerdje4nkhkjgktecvjua

Phase retrieval by pattern classification and circular mean for robust optical testing [article]

Ohgan Kim, Bong Ju Lee, Yun-Woo Lee, Ho-Soon Yang
2020 arXiv   pre-print
As the minority patterns of the wrapped phases are excluded, the effects of unknown noises can be reduced. For each cluster, the circular mean is used to calculate the denoised wrapped phases.  ...  In this paper, a new technique that applies hierarchical clustering, which classifies a few clusters according to the pattern similarity of acquired wrapped phases, is proposed.  ...  Fig. 4 . 4 Phase retrieval by pattern classification and circular mean. 3. 1 . 1 Pre-processing for pattern classificationFig. 5shows the pre-processing for the hierarchical clustering.  ... 
arXiv:2005.10997v2 fatcat:aiqar4fldjax3nmexbtdzy37km

Data mining in Raman imaging in a cellular biological system

Ya-Juan Liu, Michelle Kyne, Cheng Wang, Xi-Yong Yu
2020 Computational and Structural Biotechnology Journal  
Herein we summarize the framework for Raman imaging data analysis, which involves preprocessing, pattern recognition and validation. There are multiple methods developed for each stage of analysis.  ...  The technological developments that have led to the optimization of Raman instrumentation have helped to improve the speed of the measurement and the sensitivity.  ...  Peter Mojzeš is thanked for providing the pure spectral data of protein, lipid, and nucleus. We appreciate the support of National Natural Science Foundation of China (No.  ... 
doi:10.1016/j.csbj.2020.10.006 pmid:33163152 pmcid:PMC7595934 fatcat:h666w664vndzhoqsgfldjcijti

Automatic brain MRI segmentation scheme based on feature weighting factors selection on fuzzy c-means clustering algorithms with Gaussian smoothing

Kai Xiao, Sooi ck Ho, Andrzej Bargiela
2010 International Journal of Computational Intelligence in Bioinformatics and Systems Biology  
The method uses Gaussian smoothing to enable fuzzy c-mean (FCM) to create both a more homogeneous clustering result and reduce effect caused by noise.  ...  In addition to the observations on the clustering results of the MR images, we use validity functions and clustering centroids to evaluate the clustering results.  ...  As a result, FCM with adjustable feature weights allows validity functions to find appropriate weighting factors to correct the errors in the classification caused by noise.  ... 
doi:10.1504/ijcibsb.2010.031393 fatcat:xy3eixb6evbrhmexs5n4zurzva

Sequence Graph Transform (SGT): A Feature Embedding Function for Sequence Data Mining [article]

Chitta Ranjan, Samaneh Ebrahimi, Kamran Paynabar
2021 arXiv   pre-print
SGT features yield significantly superior results in sequence clustering and classification with higher accuracy and lower computation as compared to the existing methods, including the state-of-the-art  ...  SGT's properties are analytically proved for interpretation under normal and uniform distribution assumptions.  ...  but part of the sequence's pattern (thus, we should not set κ as a high value without a validation).  ... 
arXiv:1608.03533v15 fatcat:rnnyv76sq5fstj5zp5f5wlztoe

Mining Data of Noisy Signal Patterns in Recognition of Gasoline Bio-Based Additives using Electronic Nose

Stanisław Osowski, Krzysztof Siwek
2017 Metrology and Measurement Systems  
A special stress is put on the robustness of signal processing systems to the noise distorting the registered sensor signals.  ...  The numerical results of experiments devoted to the recognition of different blends of gasoline have shown the superiority of support vector machine in a noisy environment of measurement.  ...  clustering the measured samples and analyzing their changes caused by noise. − The supervised analysis directed to pattern recognition and classification, in order to determine the robustness of system  ... 
doi:10.1515/mms-2017-0015 fatcat:iv36dgzjsncxrifed5hg5wp5au

The Biologic Basis of Clinical Heterogeneity in Juvenile Idiopathic Arthritis

Simon W. M. Eng, Trang T. Duong, Alan M. Rosenberg, Quaid Morris, Rae S. M. Yeung
2014 Arthritis & Rheumatology  
We report a novel approach to integrating biologic and clinical data toward a new classification for childhood arthritis, using computational biology for data-driven pattern recognition. Methods.  ...  Sensitivity analysis was conducted to determine key variables in determining indicators and cluster assignment. Results were validated against an independent validation cohort. Results.  ...  Yeung had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study conception and design.  ... 
doi:10.1002/art.38875 pmid:25200124 pmcid:PMC4282094 fatcat:gtemeiumtfbslp25zr3iqov5pa

Clustering Algorithms: Brief Review in Bioinformatics

2017 International Journal of Science and Research (IJSR)  
Pattern recognition is a task of categorising some objects to a correct class based on certain measurement or features of the object.  ...  Clustering is a technique for finding similar groups in data called clusters.  ...  No funds and grants were used for this work.  ... 
doi:10.21275/art20164276 fatcat:7kzwxzktwjdmtleyfkflacnake

Fuzzy Clustering of Raman Spectral Imaging Data with a Wavelet-Based Noise-Reduction Approach

Yu-Ping Wang, Yong Wang, Paulette Spencer
2006 Applied Spectroscopy  
In this paper, we present an approach that combines a differential wavelet-based data smoothing with a fuzzy clustering algorithm for the classification of Raman spectral images.  ...  This approach is applied to the classification of spectral data collected from adhesive/dentin interface specimens where the spectral data exhibit different signal-to-noise ratios.  ...  We thank the reviewers for detailed comments that helped improve the paper.  ... 
doi:10.1366/000370206777886964 pmid:16854273 fatcat:rspnpww3off3roxuilnwnekixy

Optimizing Statistical Character Recognition Using Evolutionary Strategies to Recognize Aircraft Tail Numbers

Antonio Berlanga, Juan A. Besada, Jesús García Herrero, José M. Molina, Javier I. Portillo, José R. Casar
2004 EURASIP Journal on Advances in Signal Processing  
The design of statistical classification systems for optical character recognition (OCR) is a cumbersome task.  ...  The proposed approach is discussed and some results are obtained using a benchmark data set. This research demonstrates the successful application of ES to a difficult, noisy, and real-world problem.  ...  ACKNOWLEDGMENTS The authors recognize the support provided by AENA (Aeropuertos Españoles y Navegación Aérea) with special thanks to Angeles Varona and Germán Gonzalez for their help.  ... 
doi:10.1155/s1110865704312084 fatcat:evac6lqulffwzbpedfo3pbcyke

Support-Vector-Based Fuzzy Neural Networks

Chin-Teng Lin, Chang-Mao Yeh, Jen-Feng Chung, Sheng-Fu Liang, Her-Chang Pu
2005 International Journal of Computational Intelligence Research  
In this paper, novel fuzzy neural networks (FNNs) combining with support vector learning mechanism called support-vector-based fuzzy neural networks (SVFNNs) are proposed for pattern classification and  ...  In the second phase, the parameters of FNN are calculated by the SVM and SVR with the proposed adaptive fuzzy kernel function for pattern classification and function approximation, respectively.  ...  Using this measure, we can obtain the following criterion for the generation of a new fuzzy rule. Let b be the newly incoming pattern.  ... 
doi:10.5019/j.ijcir.2005.31 fatcat:s4clyb7errhxxkqftam2xgxzmm

Decoding Face Exemplars from fMRI Responses: What Works, What Doesn't?

J. D. Carlin
2015 Journal of Neuroscience  
Thus, there is a need to validate face effects from macaque single-unit recording against brain measurements in humans, and fMRI MVPA appears to provide the necessary sensitivity to enable this species  ...  For instance, both the single-unit and fMRI data from patch AL/AF exhibited a similar pattern of mirror image confusions.  ... 
doi:10.1523/jneurosci.1385-15.2015 pmid:26109650 pmcid:PMC4478246 fatcat:xnvoaovh3jaddgcjlgoj4rzb24

Review of Clustering Techniques in Control System

Saumya Singh, Smriti Srivastava
2020 Procedia Computer Science  
Data clustering finds application in classification of patterns in different areas such as artificial intelligence, summarization, learning, segmentation, speech recognition, pattern recognition, image  ...  Data clustering finds application in classification of patterns in different areas such as artificial intelligence, summarization, learning, segmentation, speech recognition, pattern recognition, image  ...  Type 2 fuzzy clustering algorithms (T2FCM) have proved to be insensitive to outliers and noise but does not give desired result for non-spherical clusters [1] [17] .  ... 
doi:10.1016/j.procs.2020.06.032 fatcat:z4smiadd35artavs3lvxztn4dq

Bag-of-words representation for biomedical time series classification

Jin Wang, Ping Liu, Mary F.H. She, Saeid Nahavandi, Abbas Kouzani
2013 Biomedical Signal Processing and Control  
The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag-of-words model such as local segment length and codebook size, but also robust to noise.  ...  In this work, a simple yet effective bag-of-words representation that is able to capture both local and global structure similarity information is proposed for biomedical time series representation.  ...  Furthermore, the bag-of-words representation is not only insensitive to the length of local segments and the size of codebook, but also robust to noise.  ... 
doi:10.1016/j.bspc.2013.06.004 fatcat:gzk5vace2fhkbga62c5cgdirga

Multiclass classification of microarray data with repeated measurements: application to cancer

Ka Yee Yeung, Roger E Bumgarner
2003 Genome Biology  
We show that removing highly correlated genes typically improves classification results using a small set of genes.  ...  Prediction of the diagnostic category of a tissue sample from its gene-expression profile and selection of relevant genes for class prediction have important applications in cancer research.  ...  Acknowledgements We would like to thank Sridhar Ramaswamy for providing us with the raw (.cel) files for the multiple tumor data. We also thank Jane Fridlyand for the processed NCI 60 dataset.  ... 
doi:10.1186/gb-2003-4-12-r83 pmid:14659020 pmcid:PMC329422 fatcat:j7v5qu4k3reani25lyjfwf7c3y
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