Parameter Sensitivity Analysis for the Progressive Sampling-Based Bayesian Optimization Method for Automated Machine Learning Model Selection

Abstract This paper aims at deeper exploration of the new field named auto-machine learning, as it shows promising results in specific machine learning tasks e.g. image classification. The following article is about to summarize the most successful approaches now available in the A.I. community. The automated machine learning method is very briefly described here, but the concept of automated task solving seems to be very promising, since it can significantly reduce expertise level of a person developing the machine learning model. We used Auto-Keras to find the best architecture on several datasets, and demonstrated several automated machine learning features, as well as discussed the issue deeper.

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A Machine Learning Model Selection considering Tradeoffs between Accuracy and Interpretability

10.1109/icitee53064.2021.9611872 ◽

2021 ◽

Author(s):

Zhumakhan Nazir ◽

Dinmukhamed Kaldykhanov ◽

Kozy-Korpesh Tolep ◽

Jurn-Gyu Park

Keyword(s):

Machine Learning ◽

Model Selection ◽

Learning Model ◽

Machine Learning Model

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Uncertainty Quantification and Sensitivity Analysis of Partial Charges on Macroscopic Solvent Properties in Molecular Dynamics Simulations with a Machine Learning Model

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.0c01204 ◽

2021 ◽

Author(s):

James S. Peerless ◽

Albert L. Kwansa ◽

Branden S. Hawkins ◽

Ralph C. Smith ◽

Yaroslava G. Yingling

Keyword(s):

Machine Learning ◽

Molecular Dynamics ◽

Sensitivity Analysis ◽

Molecular Dynamics Simulations ◽

Uncertainty Quantification ◽

Learning Model ◽

Machine Learning Model ◽

Partial Charges ◽

Dynamics Simulations ◽

Solvent Properties

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Trust-Based Cloud Machine Learning Model Selection For Industrial IoT and Smart City Services

IEEE Internet of Things Journal ◽

10.1109/jiot.2020.3022323 ◽

2020 ◽

pp. 1-1

Author(s):

Basheer Qolomany ◽

Ihab Mohammed ◽

Ala Al-Fuqaha ◽

Mohsen Guizani ◽

Junaid Qadir

Keyword(s):

Machine Learning ◽

Model Selection ◽

Smart City ◽

Learning Model ◽

Machine Learning Model ◽

Industrial Iot ◽

Selection For

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Machine Learning Model to Estimate Net Joint Moments during Lifting Task Using Wearable Sensors: A Preliminary Study for Design of Exoskeleton Control System

Applied Sciences ◽

10.3390/app112411735 ◽

2021 ◽

Vol 11 (24) ◽

pp. 11735

Author(s):

Seungheon Chae ◽

Ahnryul Choi ◽

Hyunwoo Jung ◽

Tae Hyong Kim ◽

Kyungran Kim ◽

...

Keyword(s):

Machine Learning ◽

Control System ◽

Musculoskeletal Diseases ◽

Lower Extremities ◽

Learning Model ◽

Classification Model ◽

Bayesian Optimization ◽

Joint Moments ◽

Machine Learning Model ◽

Starting Point

Accurately measuring the lower extremities and L5/S1 moments is important since L5/S1 moments are the principal parameters that measure the risk of musculoskeletal diseases during lifting. In this study, protocol that predicts lower extremities and L5/S1 moments with an insole sensor was proposed to replace the prior methods that have spatial constraints. The protocol is hierarchically composed of a classification model and a regression model to predict joint moments. Additionally, a single LSTM model was developed to compare with proposed protocol. To optimize hyperparameters of the machine learning model and input feature, Bayesian optimization method was adopted. As a result, the proposed protocol showed a relative root mean square error (rRMSE) of 8.06~13.88% while the single LSTM showed 9.30~18.66% rRMSE. This protocol in this research is expected to be a starting point for developing a system for estimating the lower extremity and L5/S1 moment during lifting that can replace the complex prior method and adopted to workplace environments. This novel study has the potential to precisely design a feedback iterative control system of an exoskeleton for the appropriate generation of an actuator torque.

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