R
Roberta Siciliano
Researcher at University of Naples Federico II
Publications - 82
Citations - 921
Roberta Siciliano is an academic researcher from University of Naples Federico II. The author has contributed to research in topics: Tree (data structure) & Decision tree learning. The author has an hindex of 15, co-authored 82 publications receiving 787 citations. Previous affiliations of Roberta Siciliano include Charles University in Prague.
Papers
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Journal ArticleDOI
Multivariate data analysis and modeling through classification and regression trees
Roberta Siciliano,Francesco Mola +1 more
TL;DR: A multivariate approach to binary segmentation in order to deal with more response variables and to explore dependency in multivariate data is provided.
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A fast splitting procedure for classification trees
Francesco Mola,Roberta Siciliano +1 more
TL;DR: The predictability index τ is proposed as a splitting rule for growing the same classification tree as CART does when using the Gini index of heterogeneity as an impurity measure to make a substantial saving in the time required to generate a classification tree.
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Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach
TL;DR: This work proposes an accurate heuristic algorithm called FAST that finds at least one of the consensus ranking solutions found by BB saving a lot of computational time and shows that the building block of FAST is an algorithm called QUICK that finds already one ofThe BB solutions so that it can be fruitfully considered to speed up the overall searching procedure if the number of objects is low.
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A P-spline based clustering approach for portfolio selection
TL;DR: This proposal works directly on time series without any pre-processing, except for the computation of the spline coefficients and, eventually, normalizing the series, and it is shown that the strategy is useful to support the investment decisions of financial practitioners.
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A statistical approach to growing a reliable honest tree
TL;DR: Testing procedures for both classification and regression trees are introduced and these procedures guide the search for those parts in tree structures which are statistically significant.