Soft Computing Paradigms and Regression Trees in Decision Support Systems

Soft Computing Paradigms and Regression Trees in Decision Support Systems

Cong Tran (University of South Australia, Australia), Ajith Abraham (Chung-Ang University, Korea) and Lakhmi Jain (University of South Australia, Australia)
Copyright: © 2006 |Pages: 28
DOI: 10.4018/978-1-59140-827-7.ch001
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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.

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Dedication
Table of Contents
Preface
Colin Fyfe
Chapter 1
Cong Tran, Ajith Abraham, Lakhmi Jain
Decision-making is a process of choosing among alternative courses of action for solving complicated problems where multi-criteria objectives are... Sample PDF
Soft Computing Paradigms and Regression Trees in Decision Support Systems
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Chapter 2
Ah Chung Tsoi, Phuong Kim To, Markus Hagenbuchner
This chapter describes the application of a number of text mining techniques to discover patterns in the health insurance schedule with an aim to... Sample PDF
Application of Text Mining Methodologies to Health Insurance Schedules
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Chapter 3
Quan Bai, Minjie Zhang
An intelligent agent is a reactive, proactive, autonomous, and social entity. The social ability of an agent is exercised in a multi-agent system... Sample PDF
Coordinating Agent Interactions Under Open Environments
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Chapter 4
Russell Gluck, John Fulcher
The chapter commences with an overview of automatic speech recognition (ASR), which covers not only the de facto standard approach of hidden Markov... Sample PDF
Literacy by Way of Automatic Speech Recognition
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Chapter 5
Lars Petersson, Luke Fletcher, Nick Barnes, Alexander Zelinsky
This chapter gives an overview of driver assistance systems (DAS) in general and the Smart Cars project in particular. In the Driver Assistance... Sample PDF
Smart Cars: The Next Frontier
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Chapter 6
Amanda J.C. Sharkey, Noel Sharkey
This chapter considers the application of swarm intelligence principles to collective robotics. Our aim is to identify the reasons for the growing... Sample PDF
The Application of Swarm Intelligence to Collective Robots
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Chapter 7
Mikhail Prokopenko, Geoff Poulton, Don Price
An approach to the structural health management (SHM) of future aerospace vehicles is presented. Such systems will need to operate robustly and... Sample PDF
Self-Organising Impact Sensing Networks in Robust Aerospace Vehicles
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Chapter 8
Russell Beale, Andy Pryke
This chapter argues that a knowledge discovery system should be interactive, should utilise the best in artificial intelligence (AI), evolutionary... Sample PDF
Knowledge Through Evolution
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Chapter 9
Brijesh Verma, Rinku Panchal
This chapter presents neural network-based techniques for the classification of micro-calcification patterns in digital mammograms. Artificial... Sample PDF
Neural Networks for the Classification of Benign and Malignant Patters in Digital Mammograms
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Chapter 10
Arun Khosla, Shakti Kumar, K. K. Aggarwal
Nature is a wonderful source of inspiration for building models and techniques for solving difficult problems in design, optimisation, and control.... Sample PDF
Swarm Intelligence and the Taguchi Method for Identification of Fuzzy Models
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About the Authors