A machine learning algorithm for classification under extremely scarce information
by Lev V. Utkin; Yulia A. Zhuk
International Journal of Data Analysis Techniques and Strategies (IJDATS), Vol. 4, No. 2, 2012

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.

Online publication date: Sat, 06-Sep-2014

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Data Analysis Techniques and Strategies (IJDATS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com