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By Carmela Cappelli, Francesco Mola (auth.), Prof. Dr. Hans-Hermann Bock, Prof. Marcello Chiodi, Prof. Antonino Mineo (eds.)

ISBN-10: 3540208895

ISBN-13: 9783540208891

ISBN-10: 3642171117

ISBN-13: 9783642171116

This quantity encompasses a choice of papers provided in the course of the biennial assembly of the type and information research workforce (CLADAG) of the Societa Italiana di Statistica which was once orga­ nized by way of the Istituto di Statistica of the Universita degli Studi di Palermo and held within the Palazzo Steri in Palermo on July 5-6, 2001. For this convention, and after checking the submitted four­ web page abstracts, fifty four papers have been admitted for presentation. They lined a wide variety of issues from multivariate information research, with certain emphasis on type and clustering, computa­ tional information, time sequence research, and purposes in quite a few classical or contemporary domain names. A two-fold cautious reviewing strategy resulted in the choice of twenty-two papers that are offered during this vol­ ume. they communicate both a brand new thought or method, current a brand new set of rules, or challenge an attractive program. we've got clustered those papers into 5 teams as follows: 1. category tools with functions 2. Time sequence research and similar tools three. computing device in depth strategies and Algorithms four. class and knowledge research in Economics five. Multivariate research in technologies. In each one part the papers are prepared in alphabetical order. The editors - of them the organizers of the CLADAG confer­ ence - want to convey their gratitude to the authors whose enthusiastic participation made the assembly attainable and extremely successful.

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Additional resources for Advances in Multivariate Data Analysis: Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, University of Palermo, July 5–6, 2001

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Proximity Measures Between Classification Trees 37 FREUND, Y. and SCHAPIRE, R. (1999): A Short Introduction to Boosting. Journal of Japanese Society for Artificial Intelligence, 14, 771-780. GORDON , A. D . (1999) : Classification, 2nd edition. Chapman and Hall/CRC, London. HUBERT, L. and ARABIE, P. (1985): Comparing Partitions. Journal of Classification , 2, 193-218. L. H. (1990): Cancer Diagnosis via Linear Programming. SIAM News , 23, 1-18. MIGLIO , R. (1996) : Metodi di Partizione Ricorsiva nell 'Analisi Discriminante.

The focus of this work is on the growing phase. At the basis of this phase are the concepts of impurity (or heterogeneity) within a node and of decrease of impurity deriving by splitting a node into two sub-nodes. A node is split into two sub-nodes according to the values of one out of the explanatory variables. More precisely, all the possible sub-divisions (splits) which can be defined on the basis of the explanatory variables are evaluated, and the best split, s*, is selected maximising the decrease in impurity.

Referring for simplicity to the case when Yg = g (but results simil ar to those following hold also if one refers to the case when information about the score is available) we have: G Ho(t) =L Ft(g)[l - Ft(g)] (12) 9=1 i1H o(t, s) = Ho(t) - PLHo(td - PRHo(tR) G = PLPR L[Ft(gIL) - Ft(gIR)]2 (13) g=1 Ft(gIL) and Ft(gIR) being the proportion of cases characterised by a level of Y lower than or equal to the g-th in it. and t nThe decrease in impurity in (12) is very similar to the criteria in (4) and (6): the so called anti- end- cut factor (PLPR) is combined with a measure of the dissimilarity between the two distribution of Y in t t.

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Advances in Multivariate Data Analysis: Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society, University of Palermo, July 5–6, 2001 by Carmela Cappelli, Francesco Mola (auth.), Prof. Dr. Hans-Hermann Bock, Prof. Marcello Chiodi, Prof. Antonino Mineo (eds.)


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