Towards Qualitative Assessment of Machine Learning Algorithms: Utilising Signal Modality Characterisation

A novel method for the assessment of the qualitative performance of machine learning algorithms is proposed. This is achieved by a modification of the recently proposed "delay vector variance" (DVV) method for the signal modality characterisation. Based on the local predictability in phase...

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Bibliographic Details
Main Authors: Chen, Mo, Gautama, Temujin, Van Hulle, Marc, Kuh, Anthony, Obradovic, Dragan, Mandic, Danilo
Format: Conference Proceeding
Language:English
Subjects:
Adaptive filters
Delay
Educational institutions
Heart rate variability
Machine learning algorithms
Robustness
Scattering
Signal generators
Signal processing
Signal processing algorithms
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Summary:A novel method for the assessment of the qualitative performance of machine learning algorithms is proposed. This is achieved by a modification of the recently proposed "delay vector variance" (DVV) method for the signal modality characterisation. Based on the local predictability in phase space we propose to employ the scatter diagram of DVV features in order to gauge the changes in signal nature after being processed by machine learning algorithms. A set of comprehensive simulations on representative data sets supports the analysis.
ISSN:1551-2541
2378-928X
DOI:10.1109/MLSP.2006.275576