A Greedy, Flexible Algorithm to Learn an Optimal Bayesian Network Structure

TL;DR

This paper introduces a novel heuristic algorithm for Bayesian network structure learning that balances greediness and optimality, significantly reducing computation time while maintaining near-optimal solutions.

Contribution

It presents a flexible, greedy heuristic algorithm for Bayesian network structure discovery that outperforms existing methods in speed with minimal loss of optimality.

Findings

Runs faster than previous methods

Achieves approximately 99% of optimal score

Suitable for large datasets

Abstract

In this report paper we first present a report of the Advanced Machine Learning Course Project on the provided data set and then present a novel heuristic algorithm for exact Bayesian network (BN) structure discovery that uses decomposable scoring functions. Our algorithm follows a different approach to solve the problem of BN structure discovery than the previously used methods such as Dynamic Programming (DP) and Branch and Bound to reduce the search space and find the global optima space for the problem. The algorithm we propose has some degree of flexibility that can make it more or less greedy. The more the algorithm is set to be greedy, the more the speed of the algorithm will be, and the less optimal the final structure. Our algorithm runs in a much less time than the previously known methods and guarantees to have an optimality of close to 99%. Therefore, it sacrifices less than one percent of score of an optimal structure in order to gain a much lower running time and make the algorithm feasible for large data sets (we may note that we never used any toolbox except for result validation)