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A literature review on one-class classification and its potential applications in big data
2021
Journal of Big Data
Preserved Fulltext
We observed one area that has been largely omitted in OCC-related literature is its application context for big data and its inherently associated problems, such as severe class imbalance, class rarity ...
One-class classification (OCC) is an approach to detect abnormal data points compared to the instances of the known class and can serve to address issues related to severely imbalanced datasets, which ...
Acknowledgements We would like to thank the various reviewers in the Data Mining and Machine Learning Laboratory at Florida Atlantic University, Boca Raton, FL 33431. ...
doi:10.1186/s40537-021-00514-x
fatcat:iaqfshjii5butmn64yrecd5yxq
Revisiting Feature Selection with Data Complexity for Biomedicine
[article]
2019
bioRxiv
pre-print
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We provide several guidelines to choose suitable feature selection methods for a given dataset based on its data 5 complexity measurements. ...
Despite small sample sizes, a recently proposed multilayer perceptron based deep learning method show competitive performance on multiple datasets pointing to the need of more investigations in such methods ...
The second stage is subjected to extracting the importance of each feature by solving a L1 regularized least squares problem, such that the clustering structure of the data is preserved. ...
doi:10.1101/754630
fatcat:rjdagtvn2zcg7hr4ghzts5jkya
Single-pixel interior filling function approach for detecting and correcting errors in particle tracking
2016
Proceedings of the National Academy of Sciences of the United States of America
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We demonstrate the computational gains of our method by looking at several benchmark datasets, as well as three applications involving kernel means: Euclidean embedding of distributions, class proportion ...
Additional situations and applications to experimental data are explored in SI Appendix. ...
It is based on regularized least squares with an 0 ( 2 ) penalty, which penalizes the number of nonzero groups. ...
doi:10.1073/pnas.1619104114
pmid:28028226
pmcid:PMC5240672
fatcat:imzi4n3bmjguzjakwdalknvkmi
Comprehensive Review On Twin Support Vector Machines
[article]
2021
arXiv
pre-print
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To begin with we first introduce the basic theory of support vector machine, TWSVM and then focus on the various improvements and applications of TWSVM, and then we introduce TSVR and its various enhancements ...
TWSVM is based upon the idea to identify two nonparallel hyperplanes which classify the data points to their respective classes. ...
In 2013, Peng and Xu [83] proposed robust minimum class variance TWSVM (RMCV-TWSVM) classifier to enhance generalization and robustness of TWSVM. ...
arXiv:2105.00336v2
fatcat:prxup4sbavfyxpembij6amrnka
A Unifying Review of Deep and Shallow Anomaly Detection
[article]
2020
arXiv
pre-print
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With the emergence of numerous such methods, including approaches based on generative models, one-class classification, and reconstruction, there is a growing need to bring methods of this field into a ...
Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. ...
Deep One-Class Classification Selecting kernels and hand-crafting relevant features can be challenging and quickly become impractical for complex data. ...
arXiv:2009.11732v2
fatcat:4ppfpds3ivd3bk5xcdoxmzmlie
A Unifying Review of Deep and Shallow Anomaly Detection
2021
Proceedings of the IEEE
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This article deals with application of deep learning techniques to anomaly detection. ...
ABSTRACT | Deep learning approaches to anomaly detection (AD) have recently improved the state of the art in detection performance on complex data sets, such as large Manuscript ...
Deep One-Class Classification Selecting kernels and handcrafting relevant features can be challenging and quickly become impractical for complex data. ...
doi:10.1109/jproc.2021.3052449
fatcat:i65pl2azw5dv7mtq7w7q3ylxgq
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
[article]
2019
Medical Physics (Lancaster)
pre-print
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We specifically focus on several key studies pertaining to the application of machine learning methods to biomedical image segmentation. ...
Although such classical learning models are often less accurate compared to the deep learning techniques, they are often more sample efficient and have a less complex structure. ...
., 2-norm minimization is the least square approach). ...
doi:10.1002/mp.13649
pmid:32418337
arXiv:1911.02521v1
fatcat:z6lbdtxxqzclthwu4mijo5ss3y
Cancer Classification from Gene Expression Data by NPPC Ensemble
2011
IEEE/ACM Transactions on Computational Biology & Bioinformatics
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Besides the usual majority voting method, we have introduced minimum average proximity-based decision combiner for NPPC ensemble. ...
Experimental results on cancer data sets show that the NPPC ensemble offers comparable testing accuracy to that of SVM ensemble with reduced training time on average. ...
ACKNOWLEDGMENTS The authors would like to thank the reviewers for very useful comments and suggestions which greatly improved their representation. ...
doi:10.1109/tcbb.2010.36
pmid:20479504
fatcat:6sonbfqrfbcadgmjh5e4266uz4
Deep Convolutional Hashing for Low Dimensional Binary Embedding of Histopathological Images
2018
IEEE journal of biomedical and health informatics
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In this paper, we propose a deep convolutional hashing method that can be trained "point-wise" to simultaneously learn both semantic and binary representations of histopathological images. ...
embedded features and desired binary values. ...
He is currently a PhD candidate in Department of Electrical and Computer Engineering at University of Florida, USA. ...
doi:10.1109/jbhi.2018.2827703
pmid:29993648
pmcid:PMC6429565
fatcat:ilvacjrbsba3nhu4tbqp5vqvxu
Centered Kernel Alignment Enhancing Neural Network Pretraining for MRI-Based Dementia Diagnosis
2016
Computational and Mathematical Methods in Medicine
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As a result, the performed linear embedding allows accounting for features that contribute the most to the MCI class discrimination. ...
We compare the supervised pretraining approach to two unsupervised initialization methods (autoencoders and Principal Component Analysis) and against the best four performing classification methods of ...
Computational and Mathematical Methods in Medicine ...
doi:10.1155/2016/9523849
pmid:27148392
pmcid:PMC4842359
fatcat:tl3qpjeqrjenrhd6b5y4rghm7i
Computational Diagnostic Techniques for Electrocardiogram Signal Analysis
2020
Sensors
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The procedure of ECG signals analysis is discussed in several subsections, including data preprocessing, feature engineering, classification, and application. ...
In particular, the End-to-End models integrate feature extraction and classification into learning algorithms, which not only greatly simplifies the process of data analysis, but also shows excellent accuracy ...
Regularization and tree-based methods are widely used in embedded methods. ...
doi:10.3390/s20216318
pmid:33167558
pmcid:PMC7664289
fatcat:echda3mznbekrclhwj3e774gc4
Revealing Drug-Target Interactions with Computational Models and Algorithms
2019
Molecules
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Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. ...
Methods: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based ...
Acknowledgments: We would like to thank all authors of the cited references.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/molecules24091714
fatcat:327feax5tvhsxccza47g3we75e
Involvement of Machine Learning Tools in Healthcare Decision Making
2021
Journal of Healthcare Engineering
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In healthcare for computational decision making, machine learning approaches are being used in these types of situations where a crucial data analysis needs to be performed on medical data to reveal hidden ...
With the explored knowledge, it was evident that neural network-based deep learning methods have performed extremely well in the field of computational biology with the support of the high processing power ...
Acknowledgments e authors would like to specially thank the respective staff members in the Faculty of Information Technology of University of Moratuwa who guided and assisted them to write this paper. ...
doi:10.1155/2021/6679512
pmid:33575021
pmcid:PMC7857908
fatcat:tkjpjybmife4vhugy4gq3f2tiy
Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography
[article]
2019
biorxiv/medrxiv
pre-print
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Results: Highest performance on cross-validation was observed for SZ, both on Ridge regression (ρ = 0.68, p < 0.0001) and LR (AP = 0.89, AUC = 0.92). ...
For LR, performance was almost similar in SZ (AP = 0.89, AUC = 0.86), decreased in CZ (AP = 0.71 to 0.62, AUC = 0.77 to 0.63) and increased in DZ (AP = 0.50 to 0.83, AUC = 0.72 to 0.72). ...
One method for this is bootstrapping. It is a data resampling method based on sample replacement, and it allows increasing the apparent size of the used dataset. ...
doi:10.1101/713800
fatcat:ps5tmrwjzfearmzqvuwcfiraiy
Machine Learning Based Computational Gene Selection Models: A Survey, Performance Evaluation, Open Issues, and Future Research Directions
2020
Frontiers in Genetics
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The works done in recent years to reduce the features for diagnosing tumors are discussed in detail. Furthermore, the performance of several discussed methods in the literature is analyzed. ...
One such technique is Microarray DNA. Other than being expensive, the main issue with Microarray DNA is that it generates high-dimensional data with minimal sample size. ...
study's embedded method is a
two-stage method with feature
selection and feature extraction, L1
regularization as the feature selection
method, and Partial Least Square (PLS)
as the feature extraction ...
doi:10.3389/fgene.2020.603808
pmid:33362861
pmcid:PMC7758324
fatcat:jhyfsc72tngwhnrl4vxg3k4tii
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