Dipartimento di Statistica e Metodi Quantitativi
Martedì 6 ottobre 2015 ore 12.30
U7/2104 (AULA ECONOMICA) II PIANO
Quantile-based classifiers with application to the classification of bioaerosols
CHRISTIAN HENNIG Senior Lecturer, Department of Statistical Science, University College London
Abstract Quantile classifiers are generalisations of the median-based classifiers recently introduced by Hall et al. (2009). They work for potentially high-dimensional data, and are defined by classi- fying an observation according to a sum of appropriately weighted component-wise distances of the components of the observation to the within-class quantiles. The optimal quantiles can be chosen by minimizing the misclassification error in the training sample. I will present some theory and simulations results demonstrating that quantile classifiers are very competitive. Quantile classifiers will also be applied to the detection of bioaerosol particles based on gaseous plasma electrochemistry (Sarantaridis et al., 2012). As several other classifiers, quantile classifiers aggregate information coming from the indivi- dual variables. This depends on selection and standardisation of the variables. I will discuss the- se issues in a way that may hopefully be of more general statistical interest. The first aspect is the extraction of some meaningful features from the high-dimensional and quite redundant ori- ginal form of the bioaerosol data. The second aspect is to understand standardisation as va- riable weighting, including the option to refrain from standardisation in situations where the variability of variables may be roughly proportional to the variable importance for classification, or to standardise certain groups of variables against each other, as will be suggested for the bioaerosol data.
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