Multiple Classifier Systems: First International Workshop, MCS 2000 Cagliari, Italy, June 21-23, 2000 Proceedings: 1857 (Lecture Notes in Computer Science, 1857) - Softcover

Softcover

ISBN 10: 3540677046 ISBN 13: 9783540677048

Publisher: Springer, 2000

Synopsis

Many theoretical and experimental studies have shown that a multiple classi?er system is an e?ective technique for reducing prediction errors [9,10,11,20,19]. These studies identify mainly three elements that characterize a set of cl- si?ers: -Therepresentationoftheinput(whateachindividualclassi?erreceivesby wayofinput). -Thearchitectureoftheindividualclassi?ers(algorithmsandparametri- tion). - The way to cause these classi?ers to take a decision together. Itcanbeassumedthatacombinationmethodise?cientifeachindividualcl- si?ermakeserrors'inadi?erentway',sothatitcanbeexpectedthatmostofthe classi?ers can correct the mistakes that an individual one does [1,19]. The term 'weak classi?ers' refers to classi?ers whose capacity has been reduced in some way so as to increase their prediction diversity. Either their internal architecture issimple(e.g.,theyusemono-layerperceptronsinsteadofmoresophisticated neural networks), or they are prevented from using all the information available. Sinceeachclassi?erseesdi?erentsectionsofthelearningset,theerrorcorre- tion among them is reduced. It has been shown that the majority vote is the beststrategyiftheerrorsamongtheclassi?ersarenotcorrelated.Moreover, in real applications, the majority vote also appears to be as e?cient as more sophisticated decision rules [2,13]. Onemethodofgeneratingadiversesetofclassi?ersistoupsetsomeaspect ofthetraininginputofwhichtheclassi?erisrather unstable. In the present paper,westudytwodistinctwaystocreatesuchweakenedclassi?ers;i.e.learning set resampling (using the 'Bagging' approach [5]), and random feature subset selection (using 'MFS', a Multiple Feature Subsets approach [3]). Other recent and similar techniques are not discussed here but are also based on modi?cations to the training and/or the feature set [7,8,12,21].

About this title

Synopsis

This book constitutes the refereed proceedings of the First International Workshop on Multiple Classifier Systems, MCS 2000, held in Cagliari, Italy in June 2000. The 33 revised full papers presented together with five invited papers were carefully reviewed and selected for inclusion in the book. The papers are organized in topical sections on theoretical issues, multiple classifier fusion, bagging and boosting, design of multiple classifier systems, applications of multiple classifier systems, document analysis, and miscellaneous applications.

Edition details

• Publisher: Springer
• Publication date: 2000
• Language: English
• ISBN 10: 3540677046
• ISBN 13: 9783540677048
• Binding: Paperback
• Number of pages: 420
• Editor: Kittler Josef, Roli Fabio