A machine learning algorithm for classification under extremely scarce information

Lev V. Utkin and Yulia A. Zhuk

International Journal of Data Analysis Techniques and Strategies, 2012, vol. 4, issue 2, 115-133

Abstract: When it is difficult to get learning data during the training time, we have to classify objects by having extremely small information about their feature. It is assumed in the paper that only some average or mean value of every feature and the lower and upper bounds of a set of its values are known. The main idea for constructing new classification models taking into account this information is to form a set of probability distributions bounded by some lower and upper probability distribution functions (a p-box). A discriminant function is derived in order to maximise the risk measure over the set of distributions and to minimise it over a set of classification parameters. The algorithm for classification is reduced to a parametric linear programming problem.

Keywords: imprecise probabilities; lower probability distributions; upper probability distributions; learning; risk; Bayesian inference; regression; machine learning algorithm; object classification; scarce information; discriminant functions. (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=46788 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:injdan:v:4:y:2012:i:2:p:115-133

Access Statistics for this article

More articles in International Journal of Data Analysis Techniques and Strategies from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().