A New Tool for Feature Extraction and Its Application to Credit Risk Analysis

Paweł Błaszczyk ()
Additional contact information
Paweł Błaszczyk: University of Silesia

Chapter Chapter 11 in New State of MCDM in the 21st Century, 2011, pp 131-141 from Springer

Abstract: Abstract The aim of this paper is to present a new feature extraction method. Our method is an extension of the classical Partial Least Squares (PLS) algorithm. However, a new weighted separation criterion is applied which is based on the within and between scatter matrices. In order to compare the performance of the classification the economical datasets are used.

Keywords: Classification; Credit risk; Feature extraction; Partial Least Square; Separation criterion (search for similar items in EconPapers)
Date: 2011
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:lnechp:978-3-642-19695-9_11

Ordering information: This item can be ordered from
http://www.springer.com/9783642196959

DOI: 10.1007/978-3-642-19695-9_11

Access Statistics for this chapter

More chapters in Lecture Notes in Economics and Mathematical Systems from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().