scholarly journals Gaussian Process Regression for Predictive But Interpretable Machine Learning Models: An Example of Predicting Mental Workload across Tasks

2017 ◽  
Vol 10 ◽  
Author(s):  
Matthew S. Caywood ◽  
Daniel M. Roberts ◽  
Jeffrey B. Colombe ◽  
Hal S. Greenwald ◽  
Monica Z. Weiland
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Mental Workload ◽  
Gaussian Process Regression ◽  
Learning Models ◽  
Interpretable Machine Learning ◽  
Machine Learning Models

Application of interpretable machine learning models for the intelligent decision

2019 ◽  
Vol 333 ◽  
pp. 273-283 ◽  
Author(s):  
Yawen Li ◽  
Liu Yang ◽  
Bohan Yang ◽  
Ning Wang ◽  
Tian Wu
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Interpretable Machine Learning ◽  
Intelligent Decision ◽  
Machine Learning Models

scholarly journals An Introduction on Interpretable Machine Learning

2020 ◽  
Vol 9 (7S) ◽  
pp. 107-111
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Human Life ◽  
Accuracy Score ◽  
Learning Models ◽  
Ethical Practices ◽  
Interpretable Machine Learning ◽  
Machine Learning Model ◽  
Machine Learning Applications ◽  
Machine Learning Models

As Artificial Intelligence penetrates all aspects of human life, more and more questions about ethical practices and fair uses arise, which has motivated the research community to look inside and develop methods to interpret these Artificial Intelligence/Machine Learning models. This concept of interpretability can not only help with the ethical questions but also can provide various insights into the working of these machine learning models, which will become crucial in trust-building and understanding how a model makes decisions. Furthermore, in many machine learning applications, the feature of interpretability is the primary value that they offer. However, in practice, many developers select models based on the accuracy score and disregarding the level of interpretability of that model, which can be chaotic as predictions by many high accuracy models are not easily explainable. In this paper, we introduce the concept of Machine Learning Model Interpretability, Interpretable Machine learning, and the methods used for interpretation and explanations.

scholarly journals Toward Interpretable Machine Learning Models for Materials Discovery

2019 ◽  
Vol 1 (8) ◽  
pp. 1900045 ◽  
Author(s):  
Paulius Mikulskis ◽  
Morgan R. Alexander ◽  
David Alan Winkler
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Interpretable Machine Learning ◽  
Machine Learning Models

A Machine Learning Approach to Predict the Performance of Refrigerator and Air Conditioning Using Gaussian Process Regression and Support Vector Methods

2021 ◽  
Vol 14 ◽  
Author(s):  
Harinarayan Sharma ◽  
Sonam Kumari ◽  
Aniket K. Dutt ◽  
Pawan Kumar ◽  
Mamookho E. Makhatha
Keyword(s):  
Machine Learning ◽  
Air Conditioning ◽  
Gaussian Process Regression ◽  
Coefficient Of Performance ◽  
Experimental Result ◽  
Support Vector ◽  
Nano Particle ◽  
Learning Models ◽  
Machine Learning Model ◽  
Machine Learning Models

Aim: Develop machine learning models for the performance of refrigerator and airconditioning system. Background: The Coefficient Of Performance (COP) of Refrigerator and Air-Conditioning (RAC) is a complex function of evaporative temperature and concentration of nano-particle in lubricants. In recent years, researchers focus on experimental study for improvement of COP. Further, few researchers applied simulation techniques such as fuzzy system, Artificial Neural Network (ANN), simulated annealing, etc. to the Vapour Compression Refrigeration (VCR) cycle. There is a scarcity of modeling research work for the performance of RAC system. Objective: The study aims to develop the machine learning predictive models for the performance of refrigerator and air-conditioning system using experimental data. Methods: The experiment was performed on VCR system to determine COP. Three different concentration of lubricants (added 0.5, 1.0 and 1.5g nano-TiO2 particle on 1 liter of Polyolester (POE) oil) were used. The experimentally calculated COP was used to train and test the machine learning models. Gaussian Process Regression (GPR) and Support Vector Regression (SVR) methods were applied to develop the models. Results: The experimental result reveals that the COP increases with increasing the concentration (of nano particles) at a given temperature. The addition of 0.5 and 1.0g TiO2 in the POE oil shows better rate of increment in the COP in comparison to addition of 1.5g TiO2 in the POE oil. Machine learning models using GPR and SVR with RBF kernel function is the most appropriate machine learning model for the nonlinear relationship between the output parameter (COP) and the input parameter (evaporative temperature and concentration of TiO2). Conclusion: The present study was conducted to investigate the machine learning approaches for performance of RAC system using experimental data sets. The experimental result shows that R134a and TiO2-POE nanolubricant work efficiently and the coefficient of performance of VCR system increases with concentration of nano-particle. The developed model performance is compared using coefficient of correlation and RSME values. After comparison, it is concluded that RBF based GPR model is the best fit machine learning model to predict the COP in the context of any other model for this data set.

Interpretable machine learning with reject option

2018 ◽  
Vol 66 (4) ◽  
pp. 283-290 ◽  
Author(s):  
Johannes Brinkrolf ◽  
Barbara Hammer
Keyword(s):  
Machine Learning ◽  
Vector Quantization ◽  
Random Forests ◽  
Black Box ◽  
Learning Models ◽  
Process Automation ◽  
Reject Option ◽  
Interpretable Machine Learning ◽  
Adversarial Examples ◽  
Machine Learning Models

Abstract Classification by means of machine learning models constitutes one relevant technology in process automation and predictive maintenance. However, common techniques such as deep networks or random forests suffer from their black box characteristics and possible adversarial examples. In this contribution, we give an overview about a popular alternative technology from machine learning, namely modern variants of learning vector quantization, which, due to their combined discriminative and generative nature, incorporate interpretability and the possibility of explicit reject options for irregular samples. We give an explicit bound on minimum changes required for a change of the classification in case of LVQ networks with reject option, and we demonstrate the efficiency of reject options in two examples.

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