Knowledge Through Evolution

Knowledge Through Evolution

Russell Beale (University of Birmingham, UK) and Andy Pryke (University of Birmingham, UK)
Copyright: © 2006 |Pages: 17
DOI: 10.4018/978-1-59140-827-7.ch008
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This chapter argues that a knowledge discovery system should be interactive, should utilise the best in artificial intelligence (AI), evolutionary, and statistical techniques in deriving results, but should be able to trade accuracy for understanding. Further, it needs to provide a means for users to indicate what exactly constitutes “interesting”, as well as understanding suggestions output by the computer. One such system is Haiku, which combines interactive 3D dynamic visualization and genetic algorithm techniques, and enables users to visually explore features and evaluate explanations generated by the system. Three case studies are described which illustrate the effectiveness of the Haiku system, these being Australian credit card data, Boston area housing data, and company telecommunications network call patterns. We conclude that a combination of intuitive and knowledge-driven exploration, together with conventional machine learning algorithms, offers a much richer environment, which in turn can lead to a deeper understanding of the domain under study.

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Table of Contents
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
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
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
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
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
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
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
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
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
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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