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

fYear

2006

fDate

6-8 Sept. 2006

Firstpage

365

Lastpage

370

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on

Conference_Location

Arlington, VA

ISSN

1551-2541

Print_ISBN

1-4244-0656-0

Electronic_ISBN

1551-2541

Type

conf

DOI

10.1109/MLSP.2006.275576

Filename

4053675