Probabilistic learning of boolean functions applied to the binary classification problem with categorical covariates

In this work we cast the problem of binary classification in terms of estimating a partition on Bernoulli data. When the explanatory variables are all categorical, the problem can be modelled using the language of boolean functions. We offer a probabilistic analysis of the problem, and propose two algorithms for learning boolean functions from binary data.

A machine learning approach for underwater gas leakage detection

Underwater gas reservoirs are used in many situations. In particular, Carbon Capture and Storage (CCS) facilities that are currently being developed intend to store greenhouse gases inside geological formations in the deep sea. In these formations, however, the gas might percolate, leaking back to the water and eventually to the atmosphere. The early detection of such leaks is therefore tantamount to any underwater CCS project. In this work, we propose to use Passive Acoustic Monitoring (PAM) and a machine learning approach to design efficient detectors that can signal the presence of a leakage. We use data obtained from simulation experiments off the Brazilian shore, and show that the detection based on classification algorithms achieve good performance. We also propose a smoothing strategy based on Hidden Markov Models in order to incorporate previous knowledge about the probabilities of leakage occurrences.