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Visual Analysis of Discrimination in Machine Learning [article]

Qianwen Wang, Zhenhua Xu, Zhutian Chen, Yong Wang, Shixia Liu, Huamin Qu
2020 arXiv   pre-print
In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis.  ...  The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning.  ...  In this paper, we investigate discrimination in machine learning from a visual analytics perspective and propose an interactive visualization tool, DiscriLens, to support a more comprehensive analysis.  ... 
arXiv:2007.15182v1 fatcat:d4wshqfxevhspbfjjb7dlfw7ia

dml: Distance Metric Learning in R

Yuan Tang, Tao Gao, Nan Xiao
2018 Journal of Open Source Software  
Local fisher discriminant analysis for supervised dimensionality reduction. In Proceedings of the 23rd international conference on machine learning (pp. 905-912).  ...  Semi-supervised local fisher discriminant analysis for dimensionality reduction. Machine learning, 78(1-2), 35. doi:10.1007/s10994-009-5125-7 Tang, Y. (2017). Local fisher discriminant analysis.  ... 
doi:10.21105/joss.01036 fatcat:qygrc57afvdtnnofwdumpuupye

Machine Learning and Visual Computing

Lei Zhang, Yu Cao, Fei Yang, Qiushi Zhao
2017 Applied Computational Intelligence and Soft Computing  
This special issue is dedicated to latest developments in machine learning and visual computing.  ...  Five articles from researchers around the world contribute to further steps into the theories and applications of machine learning and visual computing.  ...  This special issue is dedicated to latest developments in machine learning and visual computing.  ... 
doi:10.1155/2017/7571043 fatcat:toxniu7fazf57mhharzhai6a5y

Machine learning for big visual analysis

Jun Yu, Xue Mei, Fatih Porikli, Jason Corso
2018 Machine Vision and Applications  
Acknowledgements The work was supported in part by the NSFC-61622205 and in part the NSFC-61472110.  ...  This special issue aims to demonstrate the contribution of machine learning techniques to the research and development of big visual data analysis.  ...  Over the past years, his research interests include multimedia analysis, machine learning and image processing.  ... 
doi:10.1007/s00138-018-0948-5 fatcat:puwirktcpjg5bdfc4wxvuw77ua

Using Five Machine Learning for Breast Cancer Biopsy Predictions Based on Mammographic Diagnosis

David Oyewola, Danladi Hakimi, Kayode Adeboye, Musa Danjuma Shehu
2017 International Journal of Engineering Technologies IJET  
In this research, a mammographic diagnostic method is presented for breast cancer biopsy outcome predictions using five machine learning which includes: Logistic Regression (LR), Linear Discriminant Analysis  ...  accuracies of Logistic Regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forest (RF) methods.  ...  Our methodology involves use of machine learning techniques such as; Logistic regression (LR), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Random Forest (RF) and Support  ... 
doi:10.19072/ijet.280563 fatcat:dmlzoogvtff73mwuv2o5ce32me

Discriminant Analysis in Contrasting Dimensions for Polycystic Ovary Syndrome Prognostication [article]

Abhishek Gupta, Himanshu Soni, Raunak Joshi, Ronald Melwin Laban
2022 arXiv   pre-print
Paper also gives the analysis of data with visualizations for deeper understanding of problem.  ...  of 95.92% using Quadratic Discriminant Analysis.  ...  Boxplot [14] is known to be one of the most common form of visual representation to give a better edge in analysis of data.  ... 
arXiv:2201.03029v1 fatcat:6xbepn7qe5efxlj5ldyvxyrrgi

Vis-NIR Spectroscopy and Machine Learning Methods for the Discrimination of Transgenic Brassica napus L. and Their Hybrids with B. juncea

Soo-In Sohn, Subramani Pandian, Young-Ju Oh, John-Lewis Zinia Zaukuu, Chae-Sun Na, Yong-Ho Lee, Eun-Kyoung Shin, Hyeon-Jung Kang, Tae-Hun Ryu, Woo-Suk Cho, Youn-Sung Cho
2022 Processes  
The use of deep learning in combination with Savitzky–Golay resulted in 99.1% classification accuracy.  ...  The standard normal variate and support vector machine combination was determined to be the most accurate model in the discrimination of GM, non-GM, and hybrid plants among the many combinations (99.4%  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/pr10020240 doaj:1e368db516e140fba7c0283cca4b935d fatcat:wwfvyi64ufgbfbsyylb3qjiwa4

In-process evaluation of culture errors using morphology-based image analysis

Yuta Imai, Kei Yoshida, Megumi Matsumoto, Mai Okada, Kei Kanie, Kazunori Shimizu, Hiroyuki Honda, Ryuji Kato
2018 Regenerative Therapy  
For visualization, modified principal component analysis (PCA) was used. For prediction modeling, linear regression analysis and the MT method were applied.  ...  Moreover, we demonstrated the applicability of different concepts of machine learning using the MT method, which is effective for manufacture processes that mostly collect standard data but not a large  ...  Discrimination scoring by MT method to evaluate errors in the culture Although computational machine-learning techniques have been effectively employed in practical industrial settings, two practical essential  ... 
doi:10.1016/j.reth.2018.06.001 pmid:30525071 pmcid:PMC6222266 fatcat:vtwciqkmurcaldtkzdysfpk34m

Special Section Guest Editorial: Advances in Deep Learning for Hyperspectral Image Analysis and Classification

Masoumeh Zareappor, Jinchang Ren, Huiyu Zhou, Wankou Yang
2019 Journal of Applied Remote Sensing  
Her research interests include computer vision, image/video analysis and understanding, machine learning, hyperspectral imagery, and visual surveillance.  ...  His research interests include visual computing, computer vision, contentbased image/video analysis and understanding, machine learning, human-computer interaction, visual surveillance, archive restoration  ... 
doi:10.1117/1.jrs.13.022001 fatcat:nbcgf2aaxvfbta4onn2zufis5m

Adaptation of task difficulty in rehabilitation exercises based on the user's motor performance and physiological responses

Navid Shirzad, H. F. Machiel Van der Loos
2013 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR)  
In this paper we compared the performance of three machine learning algorithms in predicting a user's desirable difficulty during a typical reaching motion rehabilitation task.  ...  Results showed that a Neural Network approach gives higher prediction accuracy in comparison with models based on k-Nearest Neighbor and Discriminant Analysis methods.  ...  Elizabeth Croft for her feedback on this work, Arnold Yeung and Sara Sheikholeslami for their help in physiological signals processing, and the individuals who participated in this study.  ... 
doi:10.1109/icorr.2013.6650429 pmid:24187247 dblp:conf/icorr/ShirzadL13 fatcat:dgp4hgz6ijhh5jssarqhqa4dtu

Assessing Visual Field Clustering Schemes Using Machine Learning Classifiers in Standard Perimetry

Catherine Boden, Kwokleung Chan, Pamela A. Sample, Jiucang Hao, Te-Wan Lee, Linda M. Zangwill, Robert N. Weinreb, Michael H. Goldbaum
2007 Investigative Ophthalmology and Visual Science  
Two machine learning classifiers-quadratic discriminant analysis (QDA) and support vector machines with Gaussian kernel (SVMg)-were trained separately using standard perimetry data from the Diagnostic  ...  Use of clustered data showed no significant optimization of sensitivity over use of unclustered data, and no single clustering method resulted in significantly higher performance in the independent data  ...  Machine Learning Classifiers We chose to employ two MLCs with different properties, which were used in our previous studies 1,2,9,36 -39 -a quadratic discriminant analysis (QDA) and a support vector machine  ... 
doi:10.1167/iovs.06-0897 pmid:18055807 pmcid:PMC2647327 fatcat:vkrgne3p7jhnjd6kovdnn5hwv4

Clustering Visualization and Class Prediction using Flask of Benchmark Dataset for Unsupervised Techniques in Machine learning

Clustering and Dimensionality Reduction Techniques are one of the trending methods utilized in Machine Learning these days.  ...  In this paper, a comparative and predictive analysis is done utilizing three different datasets namely IRIS, Wine, and Seed from the UCI benchmark in Machine learning on four distinctive techniques.  ...  in Machine learning Acronym Meaning ML Machine Learning AI Artificial Intelligence DR Dimensionality Reduction PCA Principle Component Analysis LDA Linear Discriminant Analysis KNN K-Nearest Neighbor  ... 
doi:10.35940/ijitee.g5943.059720 fatcat:6luux5x22rfvbcsm426tkyzvte

Discrimination of Transgenic Canola (Brassica napus L.) and their Hybrids with B. rapa using Vis-NIR Spectroscopy and Machine Learning Methods

Soo-In Sohn, Subramani Pandian, John-Lewis Zinia Zaukuu, Young-Ju Oh, Soo-Yun Park, Chae-Sun Na, Eun-Kyoung Shin, Hyeon-Jung Kang, Tae-Hun Ryu, Woo-Suk Cho, Youn-Sung Cho
2021 International Journal of Molecular Sciences  
Among the different combinations, the Savitzky-Golay and Support Vector Machine combination was found to be an optimal model in the discrimination of GM, non-GM, and hybrid plants with the highest accuracy  ...  Partial least squares regression analysis of Vis-NIR spectra and biochemical characteristics showed significant correlations in their respective changes.  ...  For the effective discrimination of spectral data, several machine learning methods were used.  ... 
doi:10.3390/ijms23010220 pmid:35008646 pmcid:PMC8745187 fatcat:7rusxwzrinew7nhzvxwpi2bc54

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

Zvia Burgansky-Eliash, Gadi Wollstein, Tianjiao Chu, Joseph D. Ramsey, Clark Glymour, Robert J. Noecker, Hiroshi Ishikawa, Joel S. Schuman
2005 Investigative Ophthalmology and Visual Science  
Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output.  ...  Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model.  ...  (8) ϭ support vector machine using only 8 parameters, LDA ϭ linear discriminant analysis, LDA(8) ϭ linear discriminant analysis using only 8 parameters, RPART ϭ recursive partitioning and regression  ... 
doi:10.1167/iovs.05-0366 pmid:16249492 pmcid:PMC1941765 fatcat:d672aqxuh5fl7h4rdrvtbmujra

On Leveraging the Visual Modality for Neural Machine Translation [article]

Vikas Raunak, Sang Keun Choe, Quanyang Lu, Yi Xu, Florian Metze
2019 arXiv   pre-print
Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics.  ...  to increasing the discriminativeness between the vocabulary elements at token level prediction in NMT.  ...  To analyze the discriminativeness of the visual features for both of these datasets, we leverage an analysis mechanism used in Mu and Viswanath (2018) in the context of analyzing word embedding discriminativeness  ... 
arXiv:1910.02754v1 fatcat:cinwcz6pqzfqbifi5tisjplzle
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