A0261
Title: Machine learning methods for multivariate data analysis
Authors: Daniela Zaharie - West University of Timisoara (Romania) [presenting]
Abstract: Predictive tasks (e.g. classification or regression) can be solved by using various machine learning models (e.g. k-nearest neighbours, decision trees, support vector machines, neural networks etc). The prediction accuracy of individual models can be improved by aggregating several models using various ensemble techniques (e.g bagging, boosting, stacking). Besides these explicit ensemble techniques, there are also strategies (e.g. dropout) which induce an implicit ensemble with shared parameters by injecting extra randomness into the machine learning model and therefore generating various model instances which are then aggregated. On the other hand, in real-world applications it would be useful to provide a measure for the prediction uncertainty. Most of the machine learning models, particularly the black-box ones (e.g. neural networks), do not provide directly estimates of the prediction uncertainty. However the information provided by ensemble models can be exploited in order to estimate uncertainty measures. The aim is to provide an overview on meta-models with a focus on ensemble strategies applied to decision trees (e.g. random forests, boosted decision trees). The particularities of randomly dropping out parameters of the model and its impact on the performance are discussed. Several approaches in estimating the uncertainty of the prediction are discussed in the context of solving predictive tasks in biology and for semantic segmentation of satellite images.
