Weighting features by the value displacement rebound

Andrew Yatsko


Learning from examples draws on similarity, a concept which formalisation leads to the notion of instance space. Continuous spaces are easier to embrace since, unlike discrete, they often can be seen as hyper-constructs of 3D. Unsurprisingly, the instance-based learning methods are more developed for continuous domains than for discrete ones. The value difference metric (VDM) is one of the few examples of metrics for discrete spaces. Mixed reports about utility of VDM exist. In this paper VDM is compared with another approach where data features are weighted by the Information Gain. Some vulnerabilities of VDM are identified. A weighting method, nothing like VDM, although inspired by the former, is proposed. The results are in favour of the new weighting scheme with illustration of utility for health diagnostics.

Full Text:


DOI: https://doi.org/10.5430/air.v9n1p27


  • There are currently no refbacks.

Artificial Intelligence Research

ISSN 1927-6974 (Print)   ISSN 1927-6982 (Online)

Copyright © Sciedu Press 
To make sure that you can receive messages from us, please add the 'Sciedupress.com' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.