Business Applications And Computational Intelligence

499 Pages·2006·10.196 MB·English

by  Kevin E. Voges, Nigel Pope

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Kevin E. Voges, University of Canterbury, New Zealand
Nigel K. Ll. Pope, Griffith University, Australia
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Library of Congress Cataloging-in-Publication Data
Business applications and computational intelligence / Kevin Voges and
Nigel Pope, editors.
p. cm.
Summary: "This book deals with the computational intelligence field,
particularly business applications adopting computational intelligence
techniques"--Provided by publisher.
Includes bibliographical references and index.
ISBN 1-59140-702-8 (hardcover) -- ISBN 1-59140-703-6 (softcover)
-- ISBN 1-59140-704-4 (ebook)
1. Business--Data processing. 2. Computational intelligence.
I. Voges, Kevin, 1952- . II. Pope, Nigel.
HF5548.2.B7975 2006
658'.0563--dc22
2005023881
British Cataloguing in Publication Data
A Cataloguing in Publication record for this book is available from the British Library.
All work contributed to this book is new, previously-unpublished material. The views expressed in
this book are those of the authors, but not necessarily of the publisher.
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Preface .........................................................................................................................vii
Section I: Introduction
Chapter I
Computational Intelligence Applications in Business: A Cross-Section of the
Field................................................................................................................................1
Kevin E. Voges, University of Canterbury, New Zealand
Nigel K. Ll. Pope, Griffith University, Australia
Chapter II
Making Decisions with Data: Using Computational Intelligence within a Business
Environment ................................................................................................................ 19
Kevin Swingler, University of Stirling, Scotland
David Cairns, University of Stirling, Scotland
Chapter III
Computational Intelligence as a Platform for a Data Collection Methodology in
Management Science.................................................................................................. 38
Kristina Risom Jespersen, Aarhus School of Business, Denmark
Section II: Marketing Applications
Chapter IV
Heuristic Genetic Algorithm for Product Portfolio Planning................................... 55
Jianxin (Roger) Jiao, Nanyang Technological University, Singapore
Yiyang Zhang, Nanyang Technological University, Singapore
Yi Wang, Nanyang Technological University, Singapore
Chapter V
Modeling Brand Choice Using Boosted and Stacked Neural Networks.................... 71
Rob Potharst, Erasmus University Rotterdam, The Netherlands
Michiel van Rijthoven, Oracle Nederland BV, The Netherlands
Michiel C. van Wezel, Erasmus University Rotterdam, The Netherlands
Chapter VI
Applying Information Gathering Techniques in Business-to-Consumer and Web
Scenarios..................................................................................................................... 91
David Camacho, Universidad Autónoma de Madrid, Spain
Chapter VII
Web-Mining System for Mobile-Phone Marketing.................................................. 113
Miao-Ling Wang, Minghsin University of Science & Technology, Taiwan, ROC
Hsiao-Fan Wang, National Tsing Hua University, Taiwan, ROC
Section III: Production and Operations Applications
Chapter VIII
Artificial Intelligence in Electricity Market Operations and Management............131
Zhao Yang Dong, The University of Queensland, Australia
Tapan Kumar Saha, The University of Queensland, Australia
Kit Po Wong, The Hong Kong Polytechnic University, Hong Kong
Chapter IX
Reinforcement Learning-Based Intelligent Agents for Improved Productivity in
Container Vessel Berthing Applications .................................................................155
Prasanna Lokuge, Monash University, Australia
Damminda Alahakoon, Monash University, Australia
Chapter X
Optimization Using Horizon-Scan Technique: A Practical Case of Solving an
Industrial Problem....................................................................................................185
Ly Fie Sugianto, Monash University, Australia
Pramesh Chand, Monash University, Australia
Section IV: Data Mining Applications
Chapter XI
Visual Data Mining for Discovering Association Rules.......................................... 209
Kesaraporn Techapichetvanich, The University of Western Australia, Australia
Amitava Datta, The University of Western Australia, Australia
Chapter XII
Analytical Customer Requirement Analysis Based on Data Mining.......................227
Jianxin (Roger) Jiao, Nanyang Technological University, Singapore
Yiyang Zhang, Nanyang Technological University, Sinapore
Martin Helander, Nanyang Technological University, Singapore
Chapter XIII
Visual Grouping of Association Rules by Clustering Conditional Probabilities for
Categorical Data .......................................................................................................248
Sasha Ivkovic, University of Ballarat, Australia
Ranadhir Ghosh, University of Ballarat, Australia
John Yearwood, University of Ballarat, Australia
Chapter XIV
Support Vector Machines for Business Applications..............................................267
Brian C. Lovell, NICTA & The University of Queensland, Australia
Christian J. Walder, Max Planck Institute for Biological Cybernetics,
Germany
Chapter XV
Algorithms for Data Mining.....................................................................................291
Tadao Takaoka, University of Canterbury, New Zealand
Nigel K. Ll. Pope, Griffith University, Australia
Kevin E. Voges, University of Canterbury, New Zealand
Section V: Management Applications
Chapter XVI
A Tool for Assisting Group Decision-Making for Consensus Outcomes in
Organizations ...........................................................................................................316
Faezeh Afshar, University of Ballarat, Australia
John Yearwood, University of Ballarat, Australia
Andrew Stranieri, University of Ballarat, Australia
Chapter XVII
Analyzing Strategic Stance in Public Services Management: An Exposition of
NCaRBS in a Study of Long-Term Care Systems....................................................344
Malcolm J. Beynon, Cardiff University, UK
Martin Kitchener, University of California, USA
Chapter XVIII
The Analytic Network Process – Dependence and Feedback in Decision-Making:
Theory and Validation Examples...............................................................................360
Thomas L. Saaty, University of Pittsburgh, USA
Section VI: Financial Applications
Chapter XIX
Financial Classification Using an Artificial Immune System..................................388
Anthony Brabazon, University College Dublin, Ireland
Alice Delahunty, University College Dublin, Ireland
Dennis O’Callaghan, University College Dublin, Ireland
Peter Keenan, University College Dublin, Ireland
Michael O’Neill, University of Limerick, Ireland
Chapter XX
Development of Machine Learning Software for High Frequency Trading in
Financial Markets.....................................................................................................406
Andrei Hryshko, University of Queensland, Australia
Tom Downs, University of Queensland, Australia
Chapter XXI
Online Methods for Portfolio Selection.................................................................... 431
Tatsiana Levina, Queen’s University, Canada
Section VII: Postscript
Chapter XXII
Ankle Bones, Rogues, and Sexual Freedom for Women: Computational Intelligence
in Historial Context...................................................................................................461
Nigel K. Ll. Pope, Griffith University, Australia
Kevin E. Voges, University of Canterbury, New Zealand
About the Authors.....................................................................................................469
Index ........................................................................................................................478
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Computational intelligence (also called artificial intelligence) is a branch of computer
science that explores methods of automating behavior that can be categorized as intel-
ligent. The formal study of topics in computational intelligence (CI) has been under
way for more than 50 years. Although its intellectual roots can be traced back to Greek
mythology, the modern investigation into computational intelligence can be traced
back to the start of the computer era, when Alan Turing first asked if it would be
possible for “machinery to show intelligent behaviour.” Modern CI has many sub-
disciplines, including reasoning with uncertain or incomplete information (Bayesian
reasoning, fuzzy sets, rough sets), knowledge representation (frames, scripts, concep-
tual graphs, connectionist approaches including neural networks), and adaptive and
emergent approaches (such as evolutionary algorithms and artificial immune systems).
CI has a long history in business applications. Expert systems have been used for
decision support in management, neural networks and fuzzy logic have been used in
process control, a variety of techniques have been used in forecasting, and data mining
has become a core component of Customer Relationship Management (CRM) in mar-
keting. More recently developed agent-based applications have involved the use of
intelligent agents — Web-based shopping advisors, modelling in organizational theory
and marketing, and scenario-based planning in strategic management. Despite the ob-
vious benefits of CI to business and industry - benefits of modeling, forecasting, pro-
cess control and financial prediction to name only a few - practitioners have been slow
to take up the methods available.
Business practitioners and researchers tend to read and publish in scholarly journals
and conference proceedings in their own discipline areas. Consequently, they can be
unaware of the range of publications exploring the interaction between business and
computational intelligence. This volume addresses the need for a compact overview of
the diversity of applications of CI techniques in a number of business disciplines. The
volume consists of open-solicited and invited chapters written by leading international
researchers in the field of business applications of computational intelligence. All pa-
pers were peer reviewed by at least two recognised reviewers. The book covers some
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foundational material on computational intelligence in business, as well as technical
expositions of CI techniques. The book aims to deepen understanding of the area by
providing examples of the value of CI concepts and techniques to both theoretical
frameworks and practical applications in business. Despite the variety of application
areas and techniques, all chapters provide practical business applications.
This book reflects the diversity of the field — 43 authors from 13 countries contributed
the 22 chapters. Most fields of business are covered — marketing, data mining, e-
commerce, production and operations, finance, decision-making, and general manage-
ment. Many of the standard techniques from computational intelligence are also cov-
ered in the following chapters — association rules, neural networks, support vector
machines, evolutionary algorithms, fuzzy systems, reinforcement learning, artificial im-
mune systems, self-organizing maps, and agent-based approaches.
The 22 chapters are categorized into the following seven sections:
Section I: Introduction
Section II: Marketing Applications
Section III: Production and Operations Applications
Section IV: Data Mining Applications
Section V: Management Applications
Section VI: Financial Applications
Section VII: Postscript
Section I contains three chapters, which provide introductory material relating to CI
applications in business. Chapter I provides an overview of the field through a cross-
sectional review of the literature. It provides access to the vast and scattered literature
by citing reviews of many important CI techniques, including expert systems, artificial
neural networks, fuzzy systems, rough sets, evolutionary algorithms, and multi-agent
systems. Reviews and cited articles cover many areas in business, including finance
and economics, production and operations, marketing, and management. Chapter II
identifies important conceptual, cultural and technical barriers preventing the success-
ful commercial application of CI techniques, describes the different ways in which they
affect both the business user and the CI practitioner, and suggests a number of ways in
which these barriers may be overcome. The chapter discusses the practical conse-
quences for the business user of issues such as non-linearity and the extrapolation of
prediction into untested ranges. The aim is to highlight to technical and business
readers how their different expectations can affect the successful outcome of a CI
project. The hope is that by enabling both parties to understand each other’s perspec-
tive, the true potential of CI in a commercial project can be realized. Chapter III presents
an innovative use of CI as a method for collecting survey-type data in management
studies, designed to overcome “questionnaire fatigue.” The agent-based simulation
approach makes it possible to exploit the advantages of questionnaires, experimental
designs, role-plays, and scenarios, gaining a synergy from a combination of method-
ologies. The chapter discusses and presents a behavioral simulation based on the
agent-based simulation life cycle, which is supported by Web technology. An example
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simulation is presented for researchers and practitioners to understand how the tech-
nique is implemented.
Section II consists of four chapters illustrating marketing applications of CI (Chapters
IV to VII). Chapter IV develops a heuristic genetic algorithm for product portfolio
planning. Product portfolio planning is a critical business process in which a company
strives for an optimal mix of product offerings through various combinations of prod-
ucts and/or attribute levels. The chapter develops a practical solution method that can
find near optimal solutions and can assist marketing managers in product portfolio
decision-making. Chapter V reviews some classical methods for modeling customer
brand choice behavior, and then discusses newly developed customer behavior mod-
els, based on boosting and stacking neural network models. The new models are ap-
plied to a scanner data set of liquid detergent purchases, and their performance is
compared with previously published results. The models are then used to predict the
effect of different pricing schemes upon market share. The main advantage of these
new methods is a gain in the ability to predict expected market share. Chapter VI re-
views several fields of research that are attempting to solve a problem of knowledge
management related to the retrieval and integration of data from different electronic
sources. These research fields include information gathering and multi-agent technolo-
gies. The chapter uses a specific information gathering multi-agent system called
MAPWeb to build new Web agent-based systems that can be incorporated into busi-
ness-to-consumer activities. The chapter shows how a multi-agent system can be rede-
signed using a Web-services-oriented architecture, which allows the system to utilize
Web-service technologies. A sample example using tourism information is presented.
Chapter VII uses a data-mining information retrieval technique to create a Web-mining
system. It describes how an off-line process is used to cluster users according to their
characteristics and preferences, which then enables the system to effectively provide
appropriate information. The system uses a fuzzy c-means algorithm and information
retrieval techniques that can be used for text categorization, clustering and information
integration. The chapter describes how this system reduces the online response time in
a practical test case of a service Web site selling mobile phones. The case shows how
the proposed information retrieval technique leads to a query-response containing a
reasonable number of mobile phones purchase suggestions that best matched a user’s
preferences.
Section III contains three chapters illustrating CI applications in the general field of
production and operations (Chapters VIII to X). Chapter VIII discusses the various
techniques, such as artificial neural networks, wavelet decomposition, support vector
machines, and data mining, that can be used for the forecasting of market demand and
price in a deregulated electricity market. The chapter argues that the various tech-
niques can offer different advantages in providing satisfactory demand and price sig-
nal forecast results, depending on the specific forecasting needs. The techniques can
be applied to traditional time-series-based forecasts when the market is reasonably
stable, and can also be applied to the analysis of price spikes, which are less common
and hence more difficult to predict. Chapter IX presents a hybrid-agent model for Be-
lief-Desire-Intention agents that uses CI and interactive learning methods to handle
multiple events and intention reconsideration. In the model, the agent has knowledge
of all possible options at every state, which helps the agent to compare and switch
between options quickly if the current intention is no longer valid. The model uses a