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A literature review on one-class classification and its potential applications in big data

Naeem Seliya, Azadeh Abdollah Zadeh, Taghi M. Khoshgoftaar
2021 Journal of Big Data  
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]

Thi Ngan Dong, Lisa Winkler, Megha Khosla
2019 bioRxiv   pre-print
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

Stanislav Burov, Patrick Figliozzi, Binhua Lin, Stuart A. Rice, Norbert F. Scherer, Aaron R. Dinner
2016 Proceedings of the National Academy of Sciences of the United States of America  
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]

M. Tanveer and T. Rajani and R. Rastogi and Y.H. Shao and M. A. Ganaie
2021 arXiv   pre-print
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]

Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Grégoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Müller
2020 arXiv   pre-print
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

Lukas Ruff, Jacob R. Kauffmann, Robert A. Vandermeulen, Gregoire Montavon, Wojciech Samek, Marius Kloft, Thomas G. Dietterich, Klaus-Robert Muller
2021 Proceedings of the IEEE  
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]

Hyunseok Seo, Masoud Badiei Khuzani, Varun Vasudevan, Charles Huang, Hongyi Ren, Ruoxiu Xiao, Xiao Jia, Lei Xing
2019 Medical Physics (Lancaster)   pre-print
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

S Ghorai, A Mukherjee, S Sengupta, P K Dutta
2011 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
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

Manish Sapkota, Xiaoshuang Shi, Fuyong Xing, Lin Yang
2018 IEEE journal of biomedical and health informatics  
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

David Cárdenas-Peña, Diego Collazos-Huertas, German Castellanos-Dominguez
2016 Computational and Mathematical Methods in Medicine  
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

Liping Xie, Zilong Li, Yihan Zhou, Yiliu He, Jiaxin Zhu
2020 Sensors  
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

Liqian Zhou, Zejun Li, Jialiang Yang, Geng Tian, Fuxing Liu, Hong Wen, Li Peng, Min Chen, Ju Xiang, Lihong Peng
2019 Molecules  
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

Senerath Mudalige Don Alexis Chinthaka Jayatilake, Gamage Upeksha Ganegoda, Massimo Martorelli
2021 Journal of Healthcare Engineering  
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]

Santeri J.O. Rytky, Aleksei Tiulpin, Tuomas Frondelius, Mikko A.J. Finnilä, Sakari S. Karhula, Janina Leino, Kenneth P.H. Pritzker, Maarit Valkealahti, Petri Lehenkari, Antti Joukainen, Heikki Kröger, Heikki J. Nieminen (+1 others)
2019 biorxiv/medrxiv   pre-print
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

Nivedhitha Mahendran, P. M. Durai Raj Vincent, Kathiravan Srinivasan, Chuan-Yu Chang
2020 Frontiers in Genetics  
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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