no code implementations • 21 Mar 2024 • Sheresh Zahoor, Anthony C. Constantinou, Tim M Curtis, Mohammed Hasanuzzaman
Diabetes, a pervasive and enduring health challenge, imposes significant global implications on health, financial healthcare systems, and societal well-being.
no code implementations • 24 Jun 2023 • Kiattikun Chobtham, Anthony C. Constantinou
One of the challenges practitioners face when applying structure learning algorithms to their data involves determining a set of hyperparameters; otherwise, a set of hyperparameter defaults is assumed.
no code implementations • 11 Jun 2022 • Kiattikun Chobtham, Anthony C. Constantinou
Discovering and parameterising latent confounders represent important and challenging problems in causal structure learning and density estimation respectively.
1 code implementation • 19 Feb 2022 • Zhigao Guo, Anthony C. Constantinou
This paper describes an approximate algorithm that performs parallel sampling on Candidate Parent Sets (CPSs), and can be viewed as an extension of MINOBS which is a state-of-the-art algorithm for structure learning from high dimensional data.
no code implementations • 20 Dec 2021 • Kiattikun Chobtham, Anthony C. Constantinou, Neville K. Kitson
The algorithm assumes causal insufficiency in the presence of latent variables and produces a Partial Ancestral Graph (PAG).
no code implementations • 1 Dec 2021 • Anthony C. Constantinou, Yang Liu, Neville K. Kitson, Kiattikun Chobtham, Zhigao Guo
Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a given objective function.
no code implementations • 23 Sep 2021 • Neville K. Kitson, Anthony C. Constantinou, Zhigao Guo, Yang Liu, Kiattikun Chobtham
This paper provides a comprehensive review of combinatoric algorithms proposed for learning BN structure from data, describing 74 algorithms including prototypical, well-established and state-of-the-art approaches.
1 code implementation • 9 Jul 2021 • Yang Liu, Anthony C. Constantinou
Learning from data that contain missing values represents a common phenomenon in many domains.
no code implementations • 31 Jan 2021 • Anthony C. Constantinou, Zhigao Guo, Neville K. Kitson
Because the value of knowledge depends on what data are available, we illustrate the results both with limited and big data.
no code implementations • 25 Jan 2021 • Anthony C. Constantinou, Norman Fenton, Martin Neil
Maximising score fitting is ineffective in the presence of limited sample size because the fitting becomes increasingly distorted with limited data, guiding algorithms towards graphical patterns that share higher fitting scores and yet deviate considerably from the true graph.
1 code implementation • 19 Nov 2020 • Yang Liu, Anthony C. Constantinou, Zhigao Guo
Structure learning algorithms that learn the graph of a Bayesian network from observational data often do so by assuming the data correctly reflect the true distribution of the variables.
no code implementations • 8 Jun 2020 • Zhigao Guo, Anthony C. Constantinou
The results illustrate how different levels of pruning affect the learning speed relative to the loss in accuracy in terms of model fitting, and show that aggressive pruning may be required to produce approximate solutions for high complexity problems.
no code implementations • 29 May 2020 • Kiattikun Chobtham, Anthony C. Constantinou
Structure learning algorithms that assume causal insufficiency tend to reconstruct the ancestral graph of a BN, where bi-directed edges represent confounding and directed edges represent direct or ancestral relationships.
no code implementations • 18 May 2020 • Anthony C. Constantinou, Yang Liu, Kiattikun Chobtham, Zhigao Guo, Neville K. Kitson
This paper investigates the performance of 15 structure learning algorithms.
no code implementations • 2 Dec 2019 • Neville Kenneth Kitson, Anthony C. Constantinou
Weaknesses in the survey methodology and data available, as well as the variability in the CBNs generated by the different algorithms, mean that it is not possible to learn a definitive CBN from data.
no code implementations • 29 May 2019 • Anthony C. Constantinou
Several structure learning algorithms have been proposed towards discovering causal or Bayesian Network (BN) graphs.