Welcome to the InfoSci Platform
Reference Hub3
Soft Computing Paradigms and Regression Trees in Decision Support Systems

Soft Computing Paradigms and Regression Trees in Decision Support Systems

Cong Tran, Ajith Abraham, Lakhmi Jain
Copyright: © 2008 |Pages: 25
ISBN13: 9781599049410|ISBN10: 1599049414|EISBN13: 9781599049427
DOI: 10.4018/978-1-59904-941-0.ch058
Cite Chapter Cite Chapter

MLA

Tran, Cong, et al. "Soft Computing Paradigms and Regression Trees in Decision Support Systems." Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications, edited by Vijayan Sugumaran, IGI Global, 2008, pp. 1011-1035. https://doi.org/10.4018/978-1-59904-941-0.ch058

APA

Tran, C., Abraham, A., & Jain, L. (2008). Soft Computing Paradigms and Regression Trees in Decision Support Systems. In V. Sugumaran (Ed.), Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications (pp. 1011-1035). IGI Global. https://doi.org/10.4018/978-1-59904-941-0.ch058

Chicago

Tran, Cong, Ajith Abraham, and Lakhmi Jain. "Soft Computing Paradigms and Regression Trees in Decision Support Systems." In Intelligent Information Technologies: Concepts, Methodologies, Tools, and Applications, edited by Vijayan Sugumaran, 1011-1035. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-941-0.ch058

Export Reference

Mendeley
Favorite

Abstract

Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are involved. The past few years have witnessed a growing recognition of soft computing (SC) (Zadeh, 1998) technologies that underlie the conception, design, and utilization of intelligent systems. In this chapter, we present different SC paradigms involving an artificial neural network (Zurada, 1992) trained by using the scaled conjugate gradient algorithm (Moller, 1993), two different fuzzy inference methods (Abraham, 2001) optimised by using neural network learning/evolutionary algorithms (Fogel, 1999), and regression trees (Breiman, Friedman, Olshen, & Stone, 1984) for developing intelligent decision support systems (Tran, Abraham, & Jain, 2004). We demonstrate the efficiency of the different algorithms by developing a decision support system for a tactical air combat environment (TACE) (Tran & Zahid, 2000). Some empirical comparisons between the different algorithms are also provided.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.