Heuristics and Optimization for Knowledge Discovery

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Ruhul A. Sarker, Hussein A. Abbass, Charles Sinclair Newton
Idea Group Pub., 2002 - Business & Economics - 290 pages
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With the large amount of data stored by many organizations, capitalists have observed that this information is an intangible asset. Unfortunately, handling large databases is a very complex process and traditional learning techniques are expensive to use. Heuristic techniques provide much help in this arena, although little is known about heuristic techniques. Heuristic and Optimization for Knowledge Discovery addresses the foundation of this topic, as well as its practical uses, and aims to fill in the gap that exists in current literature.

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Contents

A Heuristic Algorithm for Feature Selection Based
13
CostSensitive Classification using Decision Trees
27
Heuristic SearchBased Stacking of Classifiers
54
Copyright

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About the author (2002)

Sarker received his PhD from DalTech, Dalhousie University, Halifax, Canada, and is currently a Senior Lecturer in Operations Research at the School of Computer Science, University of New South Wales, ADFA Campus, Canberra, Australia.

Abbass received his PhD in Computer Science from the Queensland University of Technology, Brisbane, Australia. He also holds several degrees including Business, Operational Research, and Optimisation and Constraint Logic Programming. He has gained experience in applying artificial intelligence techniques to different areas including budget planning, finance, chemical engineering, blood management, scheduling, and animal breeding and genetics.

Newton is the Head of Computer Science, University of New South Wales at the Australian Defence Force Academy campus, Canberra. He obtained his PhD in Nuclear Physics from the Australian National University, Canberra.

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