2,310 research outputs found
Interactive visualization in multiclass learning: integrating the SASSC algorithm with KLIMT
No full textClassification, Multiclass response, Subset selection, Semi-supervised learning, CART, SASSC, Phylogenetic tree, KLIMT, Interactive visualization,
Bagged Mixtures of Classifiers using Model Scoring Criteria
No full textIn the context of supervised statistical learning, we present a broad class of models named Generalised Additive Multi-Mixture Models (GAM-MM), based on a multiple combination of mixtures of classifiers to be used in both the regression and classification cases. In particular, we additively combine mixtures of different types of classifiers, defining an ensemble composed of nonparametric tools (tree- based methods), semiparametric tools (scatterplot smoothers) and parametric tools (linear regression). Within this approach, we define a classifier scoring criterion to be jointly used with the bagging procedure for estimation of the mixing parameters, and describe the GAM- MM estimation procedure, that adaptively works by iterating a backfitting-like algorithm and a local scoring procedure until convergence. The effectiveness of our approach in modelling complex data structures is evaluated by presenting the results of some applications on real and simulated data
Smoothing Score Algorithm for Generalized Additive Models
No full textIn the framework of Generalized Additive Models (GAM) an automatic
data-driven procedure is introduced for assigning an appropriate smoother to each
covariate and for defining an ordering entrance for the covariates in the model.
The resulting Smoothing Score algorithm aims to improve model indentifiability. It
uses the bagging procedure in order to select the smoothers to be assigned to each
covariate and a new scoring measure able to rank the candidate smoothers with
respect to their bagged predictive accuracy. The adequacy of this scoring measure
is evaluated on artificial data. A comparison between the smoothing score algorithm
and the standard GAM is made using real data concerning a classification task
Modelli Semiparametrici di Classificazione e Regressione Supervisionata: Alcune Proposte di Integrazione e Procedure di Stima
No full text
A NOTE ON MODEL SELECTION IN STIMA
No full textSimultaneous Threshold Interaction Modeling Algorithm (STIMA) has
been recently introduced in the framework of statistical modeling as a tool enabling
to automatically select interactions in a Generalized Linear Model (GLM) through
the estimation of a suitable defined tree structure called ”trunk”. STIMA integrates
GLM with a classification tree algorithm or a regression tree one, depending on the
nature of the response variable (nominal or numeric). Accordingly, it can be based
on the Classification Trunk Approach (CTA) or on the Regression Trunk Approach
(RTA). In both cases, interaction terms are expressed as ”threshold interactions” instead
of traditional cross-products. Compared with standard tree-based algorithms,
STIMA is based on a different splitting criterion as well as on the possibility to
”force” the first split of the trunk by manually selecting the first splitting predictor.
This paper focuses on model selection in STIMA and it introduces an alternative
model selection procedure based on a measure which evaluates the trade-off
between goodness of fit and accuracy. Its performance is compared with the one
deriving from the current implementation of STIMA by analyzing two real datasets
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