Book Chapter10.1007/978-3-642-46992-3_49
Logistic Classification Trees
Francesco Mola,Jan Klaschka,Roberta Siciliano +2 more
- 01 Jan 1996
- pp 373-378
12
TL;DR: This paper provides a methodology how to grow exploratory trees enabling to understand, through statistical modeling, which variables are the most significant for determination why an object is in one class rather than in another.
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Abstract: This paper provides a methodology how to grow exploratory trees enabling to understand, through statistical modeling, which variables are the most significant for determination why an object is in one class rather than in another. Logistic regression is used for modeling the dependence of the response dichotomous variable on the set of given predictors. The application on real data allows to discuss main advantages of the proposed procedure, especially for the analysis of real data sets whose dimensionality requires some sort of variable selection.
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Citations
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.
76
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.
63
Exploratory Versus Decision Trees
Roberta Siciliano
- 01 Jan 1998
TL;DR: Tree-structured methods using recursive partitioning procedures provide a powerful analysis tool for exploring the structure of data and for predicting the outcomes of new cases and this paper in particular recalls two-stage segmentation.
17
Posterior Prediction Modelling of Optimal Trees
Roberta Siciliano,Massimo Aria,Antonio D’Ambrosio +2 more
- 01 Jan 2008
TL;DR: A tree-based methodology that grows an optimal tree structure with the posterior prediction modelling to be used as decision rule for new objects is presented and a special case will be described in details.
11
Classification and Regression Trees: Software and New Developments
Francesco Mola
- 01 Jan 1998
TL;DR: A characterisation of the available software suitable for so called classification and regression trees methodology will be described and the general properties that an ideal programme in this domain should have are defined.
11
References
Applied Logistic Regression.
TL;DR: Applied Logistic Regression, Third Edition provides an easily accessible introduction to the logistic regression model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.
40.1K
•Book
Multivariate analysis : future directions 2
Calyampudi Radhakrishna Rao
- 01 Jan 1993
TL;DR: In this paper, the present and future of Bayesian multivariate analysis are discussed, with a brief survey and some new directions of research, G Kallianpur and J Xiong a general class of variance inequalities, S Karlin bispectral analysis of nonstationary processes, MB Priestley and MM Gabr some recent contributions to multi-target tracking, C Rao et al an approach to stochastic integration - a generalized and unified treatment, MM Rao multiivariate analysis with few or incomplete observations, JN Srivastava major challenges for multiple-response
205
Alternative strategies and CATANOVA testing in two-stage binary segmentation
Francesco Mola,Roberta Siciliano +1 more
- 01 Jan 1994
TL;DR: In the framework of binary segmentation, alternative splitting criteria based on the predictability r index of Goodman and Kruskal is introduced and used in a two-stage predictive splitting procedure.
12
Non-binary classification trees
TL;DR: This paper implements non-binary splits into Gelfand et al.'s modification of the CART algorithm to better reflect the structure of data than binary splits.
7
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