3 accepted papers
The importance of features interactions for classification tasks between two classes is proven by the abundance of high-performance machine learning methods using them. In this paper, we take our inspiration from Bayesian Networks (BNs), a famous classification method whose main asset is the graphic…
This paper proposes an understandable neural network whose score function is modeled as an additive sum of univariate spline functions. It extends usual understandable models like generative additive models, spline-based models, and neural additive models. It is shown that this neural network can be…
ReLU neural networks suffer from a problem of explainability because they partition the input space into a lot of polyhedrons. This paper proposes a constrained neural network model that replaces polyhedrons by orthotopes: each hidden neuron processes only a single component of the input signal. Whe…