By Maurizio Vichi, Paola Monari, Stefania Mignani, Angela Montanari
The amount provides new advancements in info research and class. specific awareness is dedicated to clustering, discrimination, information research and statistics, in addition to purposes in biology, finance and social sciences. The reader will locate concept and algorithms on contemporary technical and methodological advancements and lots of software papers exhibiting the empirical usefulness of the newly built strategies.
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Additional info for New Developments in Classification and Data Analysis: Proceedings of the Meeting of the Classification and Data Analysis Group (CLADAG) (Studies in Classification, Data Analysis, and Knowledge Organization)
Example text
T h e first one shows 48 Grassia the possibility to extract from data the necessary rules for the construction of the Edit plane; the second one concerns the possibility to impute data comparing complex units (not elementary units), extracted for the construction of the plane. It is so possible to reduce the computational weight of the donor imputation. The strategy makes use of tools developed in statistical methods fields for the analysis of complex structures named symbolic objects, meaning with this label both the definition of the characteristics of the constitutive elements and the connections able to link each unit with the related object.
2 Proximity-based consensus methods The consensus algorithm we consider in this paper is based on the dissimilarity measure proposed by Miglio and Soffritti (2004). This measure takes into account the partitions associated to the trees, their predictive power and the predictors used at each split. When two classification trees have to be compared, all these aspects (the structure, the partition and the predictive power) should be simultaneously considered. In fact, trees having the same distance with respect to their structures can show a very different predictive power.
In the first example, the different choices of weights and of objective functions led to the same results. The tree with the minimum mean and median dissimilarity from the 60 trees of T among 131 possible consensus trees is also the one with the minimum test set error rate (see Table 1). This tree shows a performance similar to the ones obtained through a bagging procedure applied to the bootstrap trees: its test set error rate is slightly lower than the mean value of the test set error rates associated t o these trees.