86,652 research outputs found
A two-stage Bayesian semiparametric model for novelty detection with robust prior information
No full textNovelty detection methods aim at partitioning the test units into already observed and previously unseen patterns. However, two significant issues arise: there may be considerable interest in identifying specific structures within the novelty, and contamination in the known classes could completely blur the actual separation between manifest and new groups. Motivated by these problems, we propose a two-stage Bayesian semiparametric novelty detector, building upon prior information robustly extracted from a set of complete learning units. We devise a general-purpose multivariate methodology that we also extend to handle functional data objects. We provide insights on the model behavior by investigating the theoretical properties of the associated semiparametric prior. From the computational point of view we, propose, a suitable ξ: ξ-sequence to construct an independent slice-efficient sampler that takes into account the difference between manifest and novelty components. We showcase our model performance through an extensive simulation study and applications on both multivariate and functional datasets, in which diverse and distinctive unknown patterns are discovered
Monitoring tools for robust estimation of cluster weighted models
No full textIn a robust approach to model fitting for the cluster weighted model, many choices are to be made by the statistician: specifying the shape of the clusters in the explanatory variables, assuming (or not) equal variance for the errors in the re- gression lines, and setting hyper-parameter values for the robust estimation to be protected from outliers and contamination. The most delicate hyper-parameter to specify is perhaps the percentage of trimming, or the amount of data to be excluded from the estimate, to ensure reliable inference. In this work we introduce diagnos- tic tools to help the professional, or the scientist who needs to group the data, to make an educated choice about this hyper-parameter, after a first exploration of the resulting model space
Chronic Urticaria and Celiac Disease
No full textA 4-year-old patient presented with recurrent urticaria without a clear trigger. After excluding other allergic causes, a direct link between urticaria and celiac disease was observed, a condition that affected the child who showed no gastrointestinal symptoms
Wine authenticity assessed via trimming
No full textAn authentic food is one that is what it claims to be. Consumers and
food processors need to be assured they receive exactly the specific product they pay
for. To ascertain varietal genuinity and distinguish doctored food, in this paper we
propose to employ a robust mixture estimation method. It has been shown to be a
valid tool for food authenticity studies, when applied to food data with unobserved
heterogeneity, to classify genuine wines and identify low proportions of observations
with different origins. Our methodology models the data as arising from a mixture of
Gaussian factors and employ a threshold on the multivariate density to bring apart the
less plausible data under the fitted model. Simulation results assess the effectiveness
of the proposed approach and yield very good misclassification rates when compared
to analogous methods
Monitoring tools for robust estimation of cluster weighted models = Strumenti di monitoring per la stima robusta del modello Cluster Weighted
No full textNella stima robusta di un cluster weighted model, lo statistico deve fare
molte scelte: specificare la forma dei cluster nelle variabili esplicative, assumere
(o meno) varianza uguale per gli errori nelle linee di regressione e impostare i va-
lori degli iper-parametri per la stima robusta, per evitare la distorsione generata
da valori anomali e contaminazione. L’iper-parametro pi`u delicato da specificare
`e la percentuale di trimming, ovvero la quantit`a di dati da escludere nella stima
per garantirne l’affidabilit`a. In questo lavoro introduciamo specifici strumenti dia-
gnostici per aiutare il professionista, o lo scienziato che ha bisogno di classificare
i dati, a compiere una scelta ragionata a riguardo di tale iper-parametro, anche in
base ad una prima esplorazione dello spazio delle soluzioni.In a robust approach to model fitting for the cluster weighted model, many choices are to be made by the statistician: specifying the shape of the clusters in the explanatory variables, assuming (or not) equal variance for the errors in the re- gression lines, and setting hyper-parameter values for the robust estimation to be protected from outliers and contamination. The most delicate hyper-parameter to specify is perhaps the percentage of trimming, or the amount of data to be excluded from the estimate, to ensure reliable inference. In this work we introduce diagnos- tic tools to help the professional, or the scientist who needs to group the data, to make an educated choice about this hyper-parameter, after a first exploration of the resulting model space
A robust approach to model-based classification based on trimming and constraints: Semi-supervised learning in presence of outliers and label noise
No full textIn a standard classification framework a set of trustworthy learning data are employed to build a decision rule, with the final aim of classifying unlabelled units belonging to the test set. Therefore, unreliable labelled observations, namely outliers and data with incorrect labels, can strongly undermine the classifier performance, especially if the training size is small. The present work introduces a robust modification to the Model-Based Classification framework, employing impartial trimming and constraints on the ratio between the maximum and the minimum eigenvalue of the group scatter matrices. The proposed method effectively handles noise presence in both response and exploratory variables, providing reliable classification even when dealing with contaminated datasets. A robust information criterion is proposed for model selection. Experiments on real and simulated data, artificially adulterated, are provided to underline the benefits of the proposed method
Anomaly and Novelty detection for robust semi-supervised learning
No full textThree important issues are often encountered in Supervised and Semi-Supervised Classification: class memberships are unreliable for some training units (label noise), a proportion of observations might depart from the main structure of the data (outliers) and new groups in the test set may have not been encountered earlier in the learning phase (unobserved classes). The present work introduces a robust and adaptive Discriminant Analysis rule, capable of handling situations in which one or more of the aforementioned problems occur. Two EM-based classifiers are proposed: the first one that jointly exploits the training and test sets (transductive approach), and the second one that expands the parameter estimation using the test set, to complete the group structure learned from the training set (inductive approach). Experiments on synthetic and real data, artificially adulterated, are provided to underline the benefits of the proposed method
A robust approach to model-based classification based on trimming and constraints
No full textIn a standard classification framework a set of trustworthy learning data are employed to build a decision rule, with the final aim of classifying unlabelled units belonging to the test set. Therefore, unreliable labelled observations, namely outliers and data with incorrect labels, can strongly undermine the classifier performance, especially if the training size is small. The present work introduces a robust modification to the Model-Based Classification framework, employing impartial trimming and constraints on the ratio between the maximum and the minimum eigenvalue of the group scatter matrices. The proposed method effectively handles noise presence in both response and exploratory variables, providing reliable classification even when dealing with contaminated datasets. A robust information criterion is proposed for model selection. Experiments on real and simulated data, artificially adulterated, are provided to underline the benefits of the proposed method.Science Foundation IrelandInsight Research Centre12 month embargo - A
Robust variable selection for model-based learning in presence of adulteration
No full textThe problem of identifying the most discriminating features when performing supervised learning has been extensively investigated. In particular, several methods for variable selection have been proposed in model-based classification. The impact of outliers and wrongly labeled units on the determination of relevant predictors has instead received far less attention, with almost no dedicated methodologies available. Two robust variable selection approaches are introduced: one that embeds a robust classifier within a greedy-forward selection procedure and the other based on the theory of maximum likelihood estimation and irrelevance. The former recasts the feature identification as a model selection problem, while the latter regards the relevant subset as a model parameter to be estimated. The benefits of the proposed methods, in contrast with non-robust solutions, are assessed via an experiment on synthetic data. An application to a high-dimensional classification problem of contaminated spectroscopic data is presented
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