Tomás Aluja

Knowledge Discovery with Clustering Based on Rules. Interpreting Results

It is clear that nowadays analysis of complex systems is an important handicap in Statistics, Artificial Intelligence, Information Systems, Data visualization, and other fields.

Handling missing values in kernel methods with application to microbiology data

We discuss several approaches that make possible for kernel methods to deal with missing values. The first two are extended kernels able to handle missing values without data preprocessing methods. Another two methods are derived from a sophisticated multiple imputation technique involving logistic regression as local model learner. The performance of these approaches is compared using a binary data set that arises typically in microbiology (the microbial source tracking problem). Our results show that the kernel extensions demonstrate competitive performance in comparison with multiple imputation in terms of predictive accuracy. However, these results are achieved with a simpler and deterministic methodology and entail a much lower computational effort.