Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings

2019 ◽  
Author(s):  
Thuy T. Pham
Keyword(s):  
Machine Learning ◽  
Biomedical Data ◽  
Automated Classification

Automated Classification of Societal Sentiments on Twitter with Machine Learning

2021 ◽  
pp. 1-1
Author(s):  
Piyush Vyas ◽  
Martin Reisslein ◽  
Bhaskar P. Rimal ◽  
Gitika Vyas ◽  
Ganga Prasad Basyal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Automated Classification

A Machine Learning Model for Automated Classification of Sleep Stages Using Polysomnography Signals

2021 ◽  
pp. 209-222
Author(s):  
Santosh Kumar Satapathy ◽  
Hari Kishan Kondaveeti ◽  
D. Loganathan ◽  
S. Sharathkumar
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Sleep Stages ◽  
Automated Classification ◽  
Machine Learning Model

A Data-Driven, Flexible Machine Learning Strategy for the Classification of Biomedical Data

2007 ◽  
pp. 67-85 ◽  
Author(s):  
Rajmund L. Somorjai ◽  
Murray E. Alexander ◽  
Richard Baumgartner ◽  
Stephanie Booth ◽  
Christopher Bowman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Strategy ◽  
Data Driven ◽  
Biomedical Data ◽  
Flexible Machine

scholarly journals On the cosmological performance of photometrically classified supernovae with machine learning

2020 ◽  
Vol 497 (3) ◽  
pp. 2974-2991
Author(s):  
Marcelo Vargas dos Santos ◽  
Miguel Quartin ◽  
Ribamar R R Reis
Keyword(s):  
Machine Learning ◽  
Mean Squared Error ◽  
Light Curves ◽  
Observational Cosmology ◽  
Automated Classification ◽  
Cosmological Constraints ◽  
Search Processes ◽  
Type Ia ◽  
Ia Supernovae

ABSTRACT The efficient classification of different types of supernovae is one of the most important problems for observational cosmology. However, spectroscopic confirmation of most objects in upcoming photometric surveys, such as the the Rubin Observatory Legacy Survey of Space and Time, will be unfeasible. The development of automated classification processes based on photometry has thus become crucial. In this paper, we investigate the performance of machine learning (ML) classification on the final cosmological constraints using simulated light-curves from the Supernova Photometric Classification Challenge, released in 2010. We study the use of different feature sets for the light-curves and many different ML pipelines based on either decision-tree ensembles or automated search processes. To construct the final catalogues we propose a threshold selection method, by employing a bias-variance tradeoff. This is a very robust and efficient way to minimize the mean squared error. With this method, we were able to obtain very strong cosmological constraints, which allowed us to keep $\sim 75{{\ \rm per\ cent}}$ of the total information in the Type Ia supernovae when using the SALT2 feature set, and $\sim 33{{\ \rm per\ cent}}$ for the other cases (based either on the Newling model or on standard wavelet decomposition).

A Comparison of Machine Learning Approaches for the Automated Classification of Dementia

2002 ◽  
pp. 721-722 ◽  
Author(s):  
Herbert Jelinek ◽  
David Cornforth ◽  
Patricia Waley ◽  
Eduardo Fernandez ◽  
Wayne Robinson
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Automated Classification
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