Bipartite learning is a machine learning task that aims to predict interactions between pairs of instances. It has been applied to various domains, including drug-target interactions, RNA-disease associations, and regulatory network inference. Despite being widely investigated, current methods still present drawbacks, as they are often designed for a specific application and thus do not generalize to other problems or present scalability issues. To address these challenges, we propose Oxytrees: proxy-based biclustering model trees. Oxytrees compress the interaction matrix into row- and column-wise proxy matrices, significantly reducing training time without compromising predictive performance. We also propose a new leaf-assignment algorithm that significantly reduces the time taken for prediction. Finally, Oxytrees employ linear models using the Kronecker product kernel in their leaves, resulting in shallower trees and thus even faster training. Using 15 datasets, we compared the predictive performance of ensembles of Oxytrees with that of the current state-of-the-art. We achieved up to 30-fold improvement in training times compared to state-of-the-art biclustering forests, while demonstrating competitive or superior performance in most evaluation settings, particularly in the inductive setting. Finally, we provide an intuitive Python API to access all datasets, methods and evaluation measures used in this work, thus enabling reproducible research in this field.

Due to recent technological advances, a massive amount of data is generated on a daily basis. Unfortunately, this is not always beneficial as such data may present weak-supervision, meaning that the output space can be incomplete, inexact, and inaccurate. This kind of problems is investigated in weakly-supervised learning. In this work, we explore weak-label learning, a structured output prediction task for weakly-supervised problems where positive annotations are reliable, whereas negatives are missing. For the first time in this class of problems, we investigate deep forest algorithms based on tree-embeddings, a recently proposed feature representation strategy leveraging the structure of decision trees. Furthermore, we propose two new procedures for label-imputation in each layer, named Strict Label Complement (SLC), which provides fixed conservative estimates for the number of missing labels and employs them to restrict imputations, and Fluid Label Addition (FLA), which performs such estimations on every layer and uses them to adjust the imputer’s predicted probabilities without any restrictions. We combine the new approaches with deep forest architectures to produce four new algorithms: SLCForest and FLAForest, using output space feature augmentation, and also the cascade forest embedders CaFE-SLC and CaFE-FLA, employing both tree-embeddings and the output space. Our results reveal that our methods provide superior or competitive performance to the state-of-the-art. Furthermore, we also noticed that our methods are associated with better results even in cases without weak-supervision.