GMADM-based attributes selection method in developing prediction model

Sue-Fen Huang () and Ching-Hsue Cheng ()

Quality & Quantity: International Journal of Methodology, 2013, vol. 47, issue 6, 3335-3347

Abstract: Attribute Selection is an important issue for developing a prediction model, however, how to determine an effective attribute selection algorithm is an important but difficult issue. Attribute selection can effectively delete the irrelevant and redundant attributes to increase the prediction accuracy, and evaluating attribute selection methods usually need to consider several criteria such as accuracy, type I error, and type II error. In this paper, the selected attribute process is modeled as a group multiple attributes decision making (GMADM) problem. In evaluating different GMACD methods, the most results usually are consistently, But there are some situations where the evaluated outcomes have different results. The GMADM method is useful tool for evaluating attribute selection algorithms, and the TOPSIS is capable of identifying a compromised solution when different GMADM method result in conflicting rankings. Therefore, this paper proposes an objective (persuasive) GMADM-based attributes selection method to solve this disagreement and help decision makers pick the most suitable method. After verification, the proposed model is more persuasive to evaluate the attributes selection methods for developing prediction model. Copyright Springer Science+Business Media B.V. 2013

Keywords: Attribute selection; Technique for order performance by similarity to ideal solution; Group multiple attributes decision making (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1007/s11135-012-9722-3 (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:spr:qualqt:v:47:y:2013:i:6:p:3335-3347

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-012-9722-3

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().