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Ann Macintosh, Richard Ellis, Tony Allen
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Ann Macintosh, Richard Ellis, Tony Allen
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Applications and Innovations in Intelligent Systems XII Proceedings of AI-2004, the Twenty-fourth SGAI International Conference on Innhovative Techniques and Applications of Artificial Intelligence
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Springer-Verlag
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9781846281037
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1
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CHF 121.10
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A. L. Macintosh, Napier University, UK The papers in this volume are the refereed application papers presented at ES2004, the Twenty-fourth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2004. The conference was organised by SGAI, the British Computer Society Specialist Group on Artificial Intelligence. This volume contains twenty refereed papers which present the innovative application of a range of AI techniques in a number of subject domains. This year, the papers are divided into sections on Synthesis and Prediction, Scheduling and Search, Diagnosis and Monitoring, Classification and Design, and Analysis and Evaluation This year's prize for the best refereed application paper, which is being sponsored by the Department of Trade and Industry, was won by a paper entitled 'A Case-Based Technique for Tracking Concept Drift in Spam Filtering'. The authors are Sarah Jane Delany, from the Dublin Institute of Technology, Ireland, and Padraig Cunningham, Alexey Tsymbal, and Lorcan Coyle from Trinity College Dublin, Ireland. This is the twelfth volume in the Applications and Innovations series. The Technical Stream papers are published as a companion volume under the title Research and Development in Intelligent Systems XXI. On behalf of the conference organising committee I should like to thank all those who contributed to the organisation of this year's application programme, in particular the programme committee members, the executive programme committee and our administrators Linsay Turbert and Collette Jackson.
APPLICATION PROGRAMME CHAIR'S INTRODUCTION 5 ACKNOWLEDGEMENTS 6 APPLICATIONS PROGRAMME COMMITTEE 7 CONTENTS 8 BEST APPLICATION PAPER 11 A Case-Based Technique for Tracking Concept Drift in Spam Filtering 12 1 Introduction 12 2 Spam Filtering and Machine Learning 13 3 The Problem of Concept Drift 14 3.1 Definitions and Types of Concept Drift 14 3.2 Approaches to Handling Concept Drift 14 4 A Case-Based Approach to Concept Drift 15 4.1 Feature Selection 16 4.2 Case Retrieval 16 4.3 Case-base Management 17 5 Evaluation 18 5.1 Experimental Setup 18
