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Fusion of expert and learnt knowledge in a framework of fuzzy labels

J. Lawry, J.W. Hall, R. Bovey
2004 International Journal of Approximate Reasoning  
A method for fusing data-models with expert information in the form of both certain and uncertain knowledge is proposed and applied to test problems from the fields of data classification and reliability  ...  In this framework models take the form of mass relations on joint label set space and can be inferred from data or from fuzzy label expressions.  ...  Pieter van Gelder of Delft University of Technology and Mr. Mark Klein Breteler of WL Delft Hydraulics.  ... 
doi:10.1016/j.ijar.2003.10.005 fatcat:sc56lmc3bff7lnybmdr2suz63y

A Hybrid Belief Rule-Based Classification System Based on Uncertain Training Data and Expert Knowledge

Lianmeng Jiao, Thierry Denoeux, Quan Pan
2016 IEEE Transactions on Systems, Man & Cybernetics. Systems  
With the belief rule structure, a data-driven belief rule base (DBRB) and a knowledge-driven belief rule base (KBRB) are learnt from uncertain training data and expert knowledge, respectively.  ...  In some real-world classification applications, such as target recognition, both training data collected by sensors and expert knowledge may be available.  ...  With the belief rule structure, a data-driven belief rule base (DBRB) and a knowledge-driven belief rule base (KBRB) are learnt from uncertain training data and expert knowledge, respectively.  ... 
doi:10.1109/tsmc.2015.2503381 fatcat:rwspgbosvjfz3dllvpavrelyw4

Review on the Architecture, Algorithm and Fusion Strategies in Ensemble Learning

Shruti Asmita, K.K. Shukla
2014 International Journal of Computer Applications  
Ensemble Learning is an approach in machine learning to find a predictive model taking into considerations the opinions of various experts.  ...  This document presents a review on the possible architectures that can be used to build an ensemble model, the techniques in which the opinions of the experts could be combined to give a general improved  ...  Behaviour Knowledge Space based Fusion System Behavior knowledge space (BKS) method uses lookup table for the fusion of the classifiers. There are a fixed number of classifiers in the ensemble model.  ... 
doi:10.5120/18932-0337 fatcat:ienitsbogrevlll2rd7jlfr6ry

A methodology for computing with words

Jonathan Lawry
2001 International Journal of Approximate Reasoning  
The latter is taken to be a fuzzy set on words where the membership values quantify the suitability of a particular word as a label for the value or values being considered.  ...  The approach is illustrated in a number of worked examples using various types of rules including linguistic syllogisms and a linguistic version of Jerey's rule.  ...  Tru Cao for his many helpful comments and suggestions.  ... 
doi:10.1016/s0888-613x(01)00042-1 fatcat:4fzv6iu2q5bnroeklglv5q6huy

A Bayesian Framework for the Automated Online Assessment of Sensor Data Quality

Daniel Smith, Greg Timms, Paulo De Souza, Claire D'Este
2012 Sensors  
The DBN was shown to offer a substantial average improvement (34%) in replicating the error bars that were generated by experts when compared to a fuzzy logic approach.  ...  The DBN was implemented for a particular marine deployment of temperature and conductivity sensors in Hobart, Australia.  ...  The Intelligent Sensing and Systems Laboratory is jointly funded by the Australian Government and CSIRO. This research was conducted as part of the CSIRO Wealth  ... 
doi:10.3390/s120709476 pmid:23012554 pmcid:PMC3444112 fatcat:pirdql5tovfy3f75oiqobmrtqu

SuperDeConFuse: A Supervised Deep Convolutional Transform based Fusion Framework for Financial Trading Systems [article]

Pooja Gupta, Angshul Majumdar, Emilie Chouzenoux, Giovanni Chierchia
2020 arXiv   pre-print
This results in the effective learning of a richer set of features and filters with respect to a standard convolutional neural network.  ...  Specifically, we apply a logarithm determinant regularization on the layer filters to break symmetry and force diversity in the learnt transforms, whereas we enforce the non-negativity constraint on the  ...  While in the latter case of CNNs, there is no guarantee of unique filters learnt. In this work, we propose a novel framework that can tackle those issues.  ... 
arXiv:2011.04364v1 fatcat:oig4t5iarnaudkvvsevvicntfy

CBR Supports Decision Analysis with Uncertainty [chapter]

Ning Xiong, Peter Funk
2009 Lecture Notes in Computer Science  
With such integration we create a unified framework in which CBR and decision theory can complement each other.  ...  In such a way we take advantage of both the strength of CBR to learn without domain knowledge and the ability of decision theory to analyze under uncertainty.  ...  Then the belief functions from individual cases are combined in the framework of information fusion.  ... 
doi:10.1007/978-3-642-02998-1_26 fatcat:fyk7q6xgpjfxld7daknpko25dq

An overview on the roles of fuzzy set techniques in big data processing: Trends, challenges and opportunities

Hai Wang, Zeshui Xu, Witold Pedrycz
2017 Knowledge-Based Systems  
solved in the framework of fuzzy sets. (3) Based on some principles, we infer the possible trends of using fuzzy sets in big data processing.  ...  We analyze when and why fuzzy sets work in these problems. (2) We present a critical review of the existing problems and discuss the current challenges of big data, which could be potentially and partially  ...  Acknowledgments The authors would like to thank the Editor-in-Chief and four anonymous reviewers for their insightful and constructive commendations that have led to an improved version of this paper.  ... 
doi:10.1016/j.knosys.2016.11.008 fatcat:i2ay7ugn5rfkhmz7jw6hyc67fm

Average Jane, Where Art Thou? – Recent Avenues in Efficient Machine Learning Under Subjectivity Uncertainty [chapter]

Georgios Rizos, Björn W. Schuller
2020 Communications in Computer and Information Science  
In addition, multi-target learning of different labeller tracks in parallel and/or of the uncertainty can help improve the model robustness and provide an additional uncertainty measure.  ...  As human labelling is a labour-intensive endeavour, active and cooperative learning strategies can help reduce the number of labels needed.  ...  is one more attribute of the data; to be learnt and predicted.  ... 
doi:10.1007/978-3-030-50146-4_4 fatcat:w6glenc5sfhbbof342ydshbtcu

A generic framework for semantic video indexing based on visual concepts/contexts detection

Nizar Elleuch, Anis Ben Ammar, Adel M. Alimi
2014 Multimedia tools and applications  
The third level (level3) enriches the semantic interpretation of concepts/ contexts by exploiting fuzzy knowledge.  ...  Thus, it is interesting to exploit this knowledge in order to achieve satisfactory performances. In this paper we present a generic semantic video indexing scheme, called SVI_REGIMVid.  ...  Open Access This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s)  ... 
doi:10.1007/s11042-014-1955-9 fatcat:qd2rn5x7pzgstje32nffgigkru

Assessment of the influence of adaptive components in trainable surface inspection systems

Christian Eitzinger, W. Heidl, E. Lughofer, S. Raiser, J.E. Smith, M.A. Tahir, D. Sannen, H. Van Brussel
2009 Machine Vision and Applications  
In this paper, we present a framework for the classification of images in surface inspection tasks and address several key aspects of the processing chain from the original image to the final classification  ...  Hereby, results achieved on a range of artificial and real-world test data from applications in printing, die-casting, metal processing and food production are presented.  ...  The framework shall include machine learning methods to transfer knowledge from the quality expert into the system's software.  ... 
doi:10.1007/s00138-009-0211-1 fatcat:3yjrwvhtxrdohh53ktekfgogue

Towards knowledge modeling and manipulation technologies: A survey

Andrew Thomas Bimba, Norisma Idris, Ahmed Al-Hunaiyyan, Rohana Binti Mahmud, Ahmed Abdelaziz, Suleman Khan, Victor Chang
2016 International Journal of Information Management  
A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge.  ...  A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey.  ...  Fuzzy Rule-Based System Fuzzy sets are used in representing knowledge in a fuzzy rule-based system (Cordón, 2011) .  ... 
doi:10.1016/j.ijinfomgt.2016.05.022 fatcat:hrvswtcsxzdgxdlhim4xtfggkm

Neuro-Fuzzy Classification System For Wireless-Capsule Endoscopic Images

Vassilis S. Kodogiannis, John N. Lygouras
2008 Zenodo  
The implementation of an advanced fuzzy inference neural network which combines fuzzy systems and artificial neural networks and the concept of fusion of multiple classifiers dedicated to specific feature  ...  Schemes have been developed to extract texture features from the fuzzy texture spectra in the chromatic and achromatic domains for a selected region of interest from each color component histogram of endoscopic  ...  The difficulty is that the resulting model is often as complex as the system itself making thus hard to interpret what has been learnt or to include expert knowledge.  ... 
doi:10.5281/zenodo.1073581 fatcat:6d4px2l2wfbphj5vnzgh4ozzay

Deep Adversarial Context-Aware Landmark Detection for Ultrasound Imaging [chapter]

Ahmet Tuysuzoglu, Jeremy Tan, Kareem Eissa, Atilla P. Kiraly, Mamadou Diallo, Ali Kamen
2018 Lecture Notes in Computer Science  
We have trained this network using ∼4000 labeled trans-rectal ultrasound images and tested on an independent set of images with ground truth landmark locations.  ...  In this paper, we propose a new deep learning based approach which is aimed at localizing several prostate landmarks efficiently and robustly.  ...  In multitask learning, the network must identify a set of auxiliary labels in addition to the main labels.  ... 
doi:10.1007/978-3-030-00937-3_18 fatcat:ux7c5tacmzcd7a3x24uvtn6eoy

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Changbo Zhao, Guo-Zheng Li, Chengjun Wang, Jinling Niu
2015 Evidence-Based Complementary and Alternative Medicine  
As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas.  ...  In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM).  ...  Acknowledgments This work was supported by the Natural Science Foundation of China under Grants nos. 61105053 and 61273305 as well as the Fundamental Research Funds for the Central Universities.  ... 
doi:10.1155/2015/376716 pmid:26246834 pmcid:PMC4515265 fatcat:tkr3oyc5tbhthasaej2neeyjf4
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