A survey on independence-based Markov networks learning

TL;DR

This survey reviews independence-based algorithms for learning Markov network structures from data, highlighting current methods, limitations, and open problems to enhance efficiency and quality, especially with limited data.

Contribution

It provides a comprehensive overview of state-of-the-art independence-based learning algorithms for Markov networks and discusses future research directions.

Findings

Independence-based learning enables efficient structure discovery with large, representative datasets.

Current algorithms face limitations in data scarcity and computational efficiency.

Open problems include developing formal frameworks to improve structure quality with limited data.

Abstract

This work reports the most relevant technical aspects in the problem of learning the \emph{Markov network structure} from data. Such problem has become increasingly important in machine learning, and many other application fields of machine learning. Markov networks, together with Bayesian networks, are probabilistic graphical models, a widely used formalism for handling probability distributions in intelligent systems. Learning graphical models from data have been extensively applied for the case of Bayesian networks, but for Markov networks learning it is not tractable in practice. However, this situation is changing with time, given the exponential growth of computers capacity, the plethora of available digital data, and the researching on new learning technologies. This work stresses on a technology called independence-based learning, which allows the learning of the independence structure of those networks from data in an efficient and sound manner, whenever the dataset is sufficiently large, and data is a representative sampling of the target distribution. In the analysis of such technology, this work surveys the current state-of-the-art algorithms for learning Markov networks structure, discussing its current limitations, and proposing a series of open problems where future works may produce some advances in the area in terms of quality and efficiency. The paper concludes by opening a discussion about how to develop a general formalism for improving the quality of the structures learned, when data is scarce.