  This paper presents the Relevance Feature and Vector Machine (RFVM), a novel
model that addresses the challenges of the fat-data problem when dealing with
clinical prospective studies. The fat-data problem refers to the limitations of
Machine Learning (ML) algorithms when working with databases in which the
number of features is much larger than the number of samples (a common scenario
in certain medical fields). To overcome such limitations, the RFVM incorporates
different characteristics: (1) A Bayesian formulation which enables the model
to infer its parameters without overfitting thanks to the Bayesian model
averaging. (2) A joint optimisation that overcomes the limitations arising from
the fat-data characteristic by simultaneously including the variables that
define the primal space (features) and those that define the dual space
(observations). (3) An integrated prunning that removes the irrelevant features
and samples during the training iterative optimization. Also, this last point
turns out crucial when performing medical prospective studies, enabling
researchers to exclude unnecessary medical tests, reducing costs and
inconvenience for patients, and identifying the critical patients/subjects that
characterize the disorder and, subsequently, optimize the patient recruitment
process that leads to a balanced cohort. The model capabilities are tested
against state-of-the-art models in several medical datasets with fat-data
problems. These experimental works show that RFVM is capable of achieving
competitive classification accuracies while providing the most compact subset
of data (in both terms of features and samples). Moreover, the selected
features (medical tests) seem to be aligned with the existing medical
literature.
