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By Hans-Hermann Bock (auth.), Prof. Dr. Daniel Baier, Prof. Dr. Reinhold Decker, Prof. Dr. Dr. Lars Schmidt-Thieme (eds.)

It is a brilliant privilege and enjoyment to put in writing a foreword for a ebook honor­ ing Wolfgang Gaul at the social gathering of his 60th birthday. Wolfgang Gaul is presently Professor of industrial management and administration technology and the top of the Institute of selection concept and administration technological know-how, school of Economics, collage of Karlsruhe (TH), Germany. he's, by way of any degree, the most exclusive and eminent students on the earth this day. Wolfgang Gaul has been instrumental in different top study initia­ tives and has completed an extraordinary point of good fortune in facilitating com­ munication between researchers in assorted disciplines from world wide. a very notable and distinctive point of his paintings is that he has been a number one student in such assorted parts of analysis as graph concept and internet­ paintings types, reliability conception, stochastic optimization, operations study, chance thought, sampling thought, cluster research, scaling and multivariate info research. His actions were directed not just at those and different theoretical themes, but additionally at functions of statistical and mathematical instruments to a mess of vital difficulties in machine technological know-how (e.g., w- mining), company learn (e.g., marketplace segmentation), administration technological know-how (e.g., determination help platforms) and behavioral sciences (e.g., choice mea­ surement and knowledge mining). All of his endeavors were complete on the optimum point excellence.

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These de Doctorat, Universite Paris IX Dauphine. DIDAY, E. H. Bock and E. ): Analysis of Symbolic Data. Springer, Heidelberg, 1-23. , and WINSBERG, S. (2003): Symbolic class description with interval data. Journal of Symbolic Data Analysis, 1. ] PERINEL, E. (1999): Construire un arbre de discrimination binaire a partir de donnees imprecises. Revue de Statistique Appliquee, J^l, 5-30. R. (1986): Induction of decision trees. Machine Learning, 1, 81-106. , and WINSBERG, S. (2002): Symbolic class description In: K.

We present a real example. 1 Introduction Suppose we want to describe a class, C, from a set or population of statistical units. A good way to do so would be to find the properties that characterize the class, and one way to attain that goal is to partition the class. Clustering methods are often designed to split a class of statistical units, yielding a partition into L subclasses, or clusters, where each cluster may then be described by a conjunction of properties. Partitioning methods generally fall into one of two types namely: clustering methods which optimize an intraclass homogeneity criterion, and decision trees, which optimize an inter-class criterion.

Classifiers with similar outputs are in the same cluster, and classifiers with different predicted class labels are assigned to different clusters. g. the one with the highest accuracy or the one that exhibits the maximum average distance from all other cluster centers. Finally, the selected classifiers are combined by majority voting. 4 Table 3. The H and Q coefficients and errors for 28 classifier ensembles. We have applied the cluster and select approach to 5 benchmark datasets from the Machine Learning Repository at the UCI (Blake et al.

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