Name
Abstract | ||
|---|---|---|
As artificial intelligence is being increasingly used for high-stakes applications, it is becoming more and more important that the models used be interpretable. Bayesian networks offer a paradigm for interpretable artificial intelligence that is based on probability theory. They provide a semantics that enables a compact, declarative representation of a joint probability distribution over the variables of a domain by leveraging the conditional independencies among them. The representation consists of a directed acyclic graph that encodes the conditional independencies among the variables and a set of parameters that encodes conditional distributions. This representation has provided a basis for the development of algorithms for probabilistic reasoning (inference) and for learning probability distributions from data. Bayesian networks are used for a wide range of tasks in machine learning, including clustering, supervised classification, multi-dimensional supervised classification, anomaly detection, and temporal modeling. They also provide a basis for estimation of distribution algorithms, a class of evolutionary algorithms for heuristic optimization. We illustrate the use of Bayesian networks for interpretable machine learning and optimization by presenting applications in neuroscience, the industry, and bioinformatics, covering a wide range of machine learning and optimization tasks. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.neucom.2021.01.138 | Neurocomputing |
Keywords | DocType | Volume |
Interpretability,Explainable machine learning,Probabilistic graphical models | Journal | 456 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bojan Mihaljevic | 1 | 8 | 2.91 |
| Concha Bielza | 2 | 909 | 72.11 |
| Pedro Larrañaga | 3 | 7 | 4.57 |