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Proposal for a Unified Methodology for Evaluating Supervised and Non-supervised Classification Algorithms [chapter]

Salvador Godoy-Calderón, J. Fco. Martínez-Trinidad, Manuel Lazo Cortés
2006 Lecture Notes in Computer Science  
There is presently no unified methodology that allows the evaluation of supervised and non-supervised classification algorithms.  ...  This paper proposes a unified methodology to evaluate classification problems of both kinds, that offers the possibility of making comparative evaluations and yields a larger amount of information to the  ...  Table 5 . 5 Traditional evaluation for the non-supervised experiment PE XB non-supervised experiment 0.157 0.395 Table 6 . 6 Unified methodology results for the non-supervised experiment  ... 
doi:10.1007/11892755_70 fatcat:cd4ws5tmwrcqlfr35v4jg4txqu

General Supervision via Probabilistic Transformations [article]

Santiago Mazuelas, Aritz Perez
2019 arXiv   pre-print
This paper presents a unifying framework for supervised classification with general ensembles of training data, and proposes the learning methodology of generalized robust risk minimization (GRRM).  ...  Different types of training data have led to numerous schemes for supervised classification.  ...  This paper presents a unifying framework for supervised classification with general ensembles of training data, and proposes the learning methodology of generalized RRM (GRRM).  ... 
arXiv:1901.08552v1 fatcat:xafqsvg54zeu5i4bnvhuwzajpe

Automatic Generation Of Training Data For Hyperspectral Image Classification Using Support Vector Machine

B. Abbasi, H. Arefi, B. Bigdeli, S. Roessner
2015 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
For obtaining testing data, labelled pixels was divide into two parts: test and training. Experimental result shows a final classification accuracy of about 90% using Support Vector Machine.  ...  An image classification method based on Support Vector Machine (SVM) is proposed on hyperspectral and 3K DSM data.  ...  ACKNOWLEDGEMENTS We would like to thank Deutsches Zentrum fur Luft-und Raumfahr (DLR, German Aerospace Center) for providing us the Hyspex imagery and DSM data.  ... 
doi:10.5194/isprsarchives-xl-7-w3-575-2015 fatcat:psyv7cszzffzrgwvegve23u3wi

Mining Histopathological Images via Composite Hashing and Online Learning [chapter]

Xiaofan Zhang, Lin Yang, Wei Liu, Hai Su, Shaoting Zhang
2014 Lecture Notes in Computer Science  
Specifically, we propose a principled framework to unify hashing-based image retrieval and supervised learning.  ...  Upon a local data subset that retains the retrieved images, supervised learning methods are applied on-the-fly to model image structures for accurate classification.  ...  Therefore, we further propose to employ supervised learning methods on-the-fly to build a finer model in the retrieved set for accurate classification and diagnosis.  ... 
doi:10.1007/978-3-319-10470-6_60 fatcat:iadlmt7fyvd2rg2actcwa7wxpa

An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Tatireddy Reddy, Jonnadula Harikiran
2022 Journal of Spectral Imaging  
As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification.  ...  Furthermore, this review will assist as a reference for future research to improve the classification and dimensionality reduction approaches.  ...  For performance assessment, the authors combined the adequately reflect the non-linear feature of a hyperspectral image. Paul and Chaki 71 proposed a method based on pooling.  ... 
doi:10.1255/jsi.2022.a1 fatcat:rue5klkmlfcrzftepc6lzfcbfe

Retinal Image Classification by Self-supervised Fuzzy Clustering Network

Yueguo Luo, Jing Pan, Shaoshuai Fan, Zeyu Du, Guanghua Zhang
2020 IEEE Access  
Specifically, we propose a Self-supervised Fuzzy Clustering Network (SFCN) by a feature learning module, reconstruction module, and a fuzzy self-supervision module.  ...  The feature learning and reconstruction modules ensure the representative ability of the network, and fuzzy self-supervision module is in charge of further providing the training direction for the whole  ...  AUC is a widely used evaluation metric for examining performance of the classification model.  ... 
doi:10.1109/access.2020.2994047 fatcat:t77mpusgerb5tb2g7vuitxdseq

Machine Learning Methods with Noisy, Incomplete or Small Datasets

Cesar F. Caiafa, Zhe Sun, Toshihisa Tanaka, Pere Marti-Puig, Jordi Solé-Casals
2021 Applied Sciences  
Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others.  ...  We believe that this special issue will bring new ideas for solving this challenging problem, and will provide clear examples of application in real-world scenarios.  ...  (Japan-China) proposed a unified and practical framework for knowledge inference inside the smart building.  ... 
doi:10.3390/app11094132 doaj:b756026d4f1b45e89f158fe4378f7e8c fatcat:zpqxuxf5ora2xk3zpmge73tyl4

Automated Machine Learning – a brief review at the end of the early years [article]

Hugo Jair Escalante
2020 arXiv   pre-print
More specifically, in this chapter an introduction to AutoML for supervised learning is provided and an historical review of progress in this field is presented.  ...  In the context of supervised learning, AutoML is concerned with feature extraction, pre processing, model design and post processing.  ...  Acknowledgments The author is grateful with the editors for their support in the preparation of this manuscript.  ... 
arXiv:2008.08516v3 fatcat:xygxkejxvvd5joz2nouxya5ska

Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI [article]

Mohit Kumar, Bernhard A. Moser, Lukas Fischer, Bernhard Freudenthaler
2022 arXiv   pre-print
A unified approach to "privacy-preserving interpretable and transferable learning" is considered for studying and optimizing the tradeoffs between privacy, interpretability, and transferability aspects  ...  A variational membership-mapping Bayesian model is used for the analytical approximations of the defined information theoretic measures for privacy-leakage, interpretability, and transferability.  ...  AI for Healthcare Systems); the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK); the Federal Ministry for Digital and Economic Affairs (BMDW); and the  ... 
arXiv:2106.06046v5 fatcat:lm4irfkervgilds6ly65c624vq

Three-Way Decision for Handling Uncertainty in Machine Learning: A Narrative Review [chapter]

Andrea Campagner, Federico Cabitza, Davide Ciucci
2020 Lecture Notes in Computer Science  
of strength of the three-way methodology, and the opportunities for further research.  ...  In this work we introduce a framework, based on threeway decision (TWD) and the trisecting-acting-outcome model, to handle uncertainty in Machine Learning (ML).  ...  , robust and non-invasive manner [20] , while also obtaining higher predictive accuracy than with traditional ML methodologies.  ... 
doi:10.1007/978-3-030-52705-1_10 fatcat:4nbk2zffkjevbbdv7l5zpofi5i

Min-Entropy Latent Model for Weakly Supervised Object Detection

Fang Wan, Pengxu Wei, Jianbin Jiao, Zhenjun Han, Qixiang Ye
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition  
In this paper, a min-entropy latent model (MELM) is proposed for weakly supervised object detection.  ...  MELM is unified with feature learning and optimized with a recurrent learning algorithm, which progressively transfers the weak supervision to object locations.  ...  Acknowledgements: This work is partially supported by the NSFC under Grant 61671427, 61771447, 61601466, and Beijing Municipal Science and Technology Commission.  ... 
doi:10.1109/cvpr.2018.00141 dblp:conf/cvpr/WanWJHY18 fatcat:s4zwwrfi5naonh6osgaemw4u7m

Unsupervised Anomaly Detection of Healthcare Providers Using Generative Adversarial Networks [chapter]

Krishnan Naidoo, Vukosi Marivate
2020 Lecture Notes in Computer Science  
This study evaluates previous anomaly detection machine learning models and proposes an unsupervised framework to identify anomalies using a Generative Adversarial Network (GANs) model.  ...  Healthcare fraud is considered a challenge for many societies.  ...  Section 3 presents our proposed methodology highlighting the GANS architecture, anomaly score function, algorithms, data sets used, data pre-processing and performance metrics.  ... 
doi:10.1007/978-3-030-44999-5_35 fatcat:qxuczgunonaazh7a6ei6saifze

Editorial

A. Famili
2011 Intelligent Data Analysis  
Pichara and Soto discuss the importance of anomaly detection in large commercial data sets and propose a new semi-supervised algorithm that actively learns to detect anomalies through interaction with  ...  They apply their methods to a real data set of private companies where their learning algorithm is trained and calibrated in a supervised way.  ...  We look forward to receiving your feedback along with more and more quality articles in both applied and theoretical research. With our best wishes, Dr. A. Famili Editor-in-Chief  ... 
doi:10.3233/ida-2010-0458 fatcat:sucdynf6drervdo45gyqyae4oq

SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling [article]

Haowen Lin, Jian Lou, Li Xiong, Cyrus Shahabi
2021 arXiv   pre-print
We propose a new framework dubbed SemiFed that unifies two dominant approaches for semi-supervised learning: consistency regularization and pseudo-labeling.  ...  SemiFed takes advantage of the federation so that for a given image, the pseudo-label holds only if multiple models from different clients produce a high-confidence prediction and agree on the same label  ...  We propose Semi-Supervised Federated Learning (SemiFed) as a unified framework and apply it to image classification with limited labeled samples.  ... 
arXiv:2108.09412v1 fatcat:4teau6zlubbzpcpgpx5bobmu2u

Editorial

2015 Intelligent Data Analysis  
Ros et al. in the next article propose a new instance selection algorithm that is primarily suitable for classification in non-trivial data sizes.  ...  Lee in the next article of this group presents a novel methodology for sequential classification, a two stage process, which involves sequential pattern generation and classification.  ... 
doi:10.3233/ida-150726 fatcat:ydwsfst2mjdldm6x2wk3cdg2bi
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