Mar 13, 2020 · Our approach provides a new way to unveil the (possibly latent) characteristics of various machine learning systems, by explicitly considering ...
1) We proposed an MT-based approach (METTLE) to assess- ing and validating unsupervised machine learning systems that generally suffer from the absence of a ...
People also ask
What is metamorphic testing used for?
Metamorphic testing (MT) is a property-based software testing technique, which can be an effective approach for addressing the test oracle problem and test case generation problem.
What is the test Oracle of metamorphic testing?
Metamorphic testing (MT) reduces the need for an Oracle by using the relationship between two or more inputs (derived from the original input) and their expected outputs. These relationships are more general and can help increase test coverage. They are also known as metamorphic relations (MR).
How to validate data for machine learning?
How to validate machine learning models
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Step 1: Load the required libraries and modules. ...
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Step 2: Read the data and perform basic data checks. ...
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Step 3: Create arrays for the features and the response variable. ...
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Step 4: Try out various validation techniques. ...
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Step 5: Set up and run TFMA using Keras.
What is the key difference between supervised and unsupervised learning?
The biggest difference between supervised and unsupervised machine learning is the type of data used. Supervised learning uses labeled training data, and unsupervised learning does not. More simply, supervised learning models have a baseline understanding of what the correct output values should be.
This article develops a METamorphic Testing approach to assessing and validating unsupervised machine LEarning systems, abbreviated as mettle, ...
METTLE: A METamorphic Testing Approach to Assessing and Validating Unsupervised Machine Learning Systems. Authors. Xie, Xiaoyuan; Zhang, Zhiyi; Chen, Tsong ...
In this paper, the authors developed a MET amorphic approach to assess and validate unsupervised machine learning systems, abbreviated as mettle, ...
METTLE: a METamorphic Testing approach to assessing & validating unsupervised machine LEarning systems ... machine LEarning systems, abbreviated as METTLE. Our ...
METTLE: a. METamorphic testing approach to assessing and validating unsupervised machine. LEarning systems. IEEE Transactions on Reliability, 69(4), 1293-1322 ...
A theoretical analysis of the risk evaluation formulas for spectrum-based fault localization · Testing and validating machine learning classifiers by metamorphic ...
Along this line, in this paper, we present a metamorphic testing based method for validating and characterizing unsupervised machine learning programs, and ...
METTLE: a METamorphic Testing approach to assessing and validating unsupervised machine LEarning systems - Xiaoyuan Xie, Zhiyi Zhang, Tsong Yueh Chen, Yang ...