rFBP: Replicated Focusing Belief Propagation algorithm

The rFBP project implements a scikit-learn compatible machine-learning binary classifier leveraging fully connected neural networks with a learning algorithm (Replicated Focusing Belief Propagation, rFBP) that is quickly converging and robust (less prone to brittle overfitting) for ill-posed datasets (very few samples compared to the number of features). The current implementation works only with binary features such as one-hot encoding for categorical data. This library has already been widely used to successfully predict source attribution starting from GWAS (Genome Wide Association Studies) data. That study was trying to predict the animal origin for an infectious bacterial disease inside the H2020 European project COMPARE (Grant agreement ID: 643476). A full description of the pipeline used in this study is available in the abstract and slides provided into the publications folder of the project. Algorithm application on real data: Classification of Genome Wide Association data by Belief Propagation Neural network, CCS Italy 2019, Conference paper Classification of Genome Wide Association data by Belief Propagation Neural network, CCS Italy 2019, Conference slides