Title :
Towards Qualitative Assessment of Machine Learning Algorithms: Utilising Signal Modality Characterisation
Author :
Chen, Mo ; Gautama, Temujin ; Van Hulle, M. ; Kuh, Anthony ; Obradovic, Dragan ; Mandic, Danilo
Author_Institution :
Dept. of Electr. & Electron. Eng., Imperial Coll. London, London
Abstract :
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.
Keywords :
adaptive filters; learning (artificial intelligence); vectors; adaptive filter; delay vector variance method; machine learning algorithm; qualitative assessment; signal modality characterisation; Adaptive filters; Delay; Educational institutions; Heart rate variability; Machine learning algorithms; Robustness; Scattering; Signal generators; Signal processing; Signal processing algorithms;
Conference_Titel :
Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on
Conference_Location :
Arlington, VA
Print_ISBN :
1-4244-0656-0
Electronic_ISBN :
1551-2541
DOI :
10.1109/MLSP.2006.275576
