Morphological image operators are a class of non-linear image mappings studied in Mathematical Morphology. Many significant theoretical results regarding the characterization …
An important problem in bioinformatics is the inference of gene regulatory networks (GRNs) from expression profiles. In general, the main limitations faced by GRN inference methods …
A classical approach to designing binary image operators is mathematical morphology (MM). We propose the Discrete Morphological Neural Networks (DMNN) for binary image …
We introduce a fast Branch-and-Bound algorithm for optimal feature selection based on a U- curve assumption for the cost function. The U-curve assumption, which is based on the …
In Machine Learning, feature selection is an important step in classifier design. It consists of finding a subset of features that is optimum for a given cost function. One possibility to solve …
The U-curve optimization problem is characterized by a decomposable in U-shaped curves cost function over the chains of a Boolean lattice. This problem can be applied to model the …
In this paper, we introduce featsel, a framework for benchmarking of feature selection algorithms and cost functions. This framework allows the user to deal with the search space …
D Marcondes, J Barrera - International Conference on Discrete Geometry …, 2024 - Springer
The machine learning of lattice operators has three possible bottlenecks. From a statistical standpoint, it is necessary to design a constrained class of operators based on prior …
Cross-validation techniques for risk estimation and model selection are widely used in statistics and machine learning. However, the understanding of the theoretical properties of …