Algorithm FRiS-TDR for Generalized Classification of the Labeled, Semi-labeled and Unlabeled Datasets

I. A. Borisova () and N. G. Zagoruiko ()
Additional contact information
I. A. Borisova: Sobolev Institute of Mathematics SD RAS
N. G. Zagoruiko: Sobolev Institute of Mathematics SD RAS

A chapter in Clusters, Orders, and Trees: Methods and Applications, 2014, pp 151-165 from Springer

Abstract: Abstract The problem of generalized classification combines three well-known problems of machine learning: classification (supervised learning), clustering (unsupervised learning), and semi-supervised learning. These problems differ from each other based on the ratio of labeled and unlabeled objects in a training dataset. In the classification problem all the objects are labeled, and in the clustering problem all the objects are unlabeled. Semi-supervised learning makes use of both labeled and unlabeled objects for training—typically a small amount of labeled objects with a large amount of unlabeled objects. Usually these problems are examined separately and special algorithms are developed for solving each of them. Algorithm FRiS-taxonomy decision rule based on function of rival similarity examines these three problems as special cases of the generalized classification problem and solves all of them. This algorithm automatically determines the number of clusters and finds effective decision rules independently of the ratio of labeled and unlabeled samples in datasets.

Keywords: FRiS-function; Semi-supervised learning; Clustering; Classification; Generalized classification (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:spr:spochp:978-1-4939-0742-7_9

Ordering information: This item can be ordered from
http://www.springer.com/9781493907427

DOI: 10.1007/978-1-4939-0742-7_9

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().