Towards the genome-scale discovery of bivariate monotonic classifiers

Océane Fourquet, Martin S. Krejca, Carola Doerr, Benno Schwikowski

Abstract: Bivariate monotonic classifiers (BMCs) are based on pairs of input features. Like many other models used for machine learning, they can capture nonlinear patterns in high-dimensional data. At the same time, they are simple and easy to interpret. Until now, the use of BMCs on a genome scale was hampered by the high computational complexity of the search for pairs of features with a high leave-one-out performance estimate.

External IDs:dblp:journals/bmcbi/FourquetKDS25