Uma métrica fuzzy para aprendizagem de estruturas de redes bayesianas pelo método de Monte Carlo e cadeias de Markov

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2014.A aprendizagem de estrutura de redes bayesianas (RB) a partir dos dados é considerada uma tarefa complexa, uma vez que o número de estruturas possíveis cresce exponencialmente de acordo com o número de variáveis. Existem dois métodos principais para esta tarefa de aprendizagem de estruturas de RB: o método de independência condicional, que busca uma estrutura consistente com os testes de independência realizados nos dados; o método de busca heurística, que explora o espaço de busca avaliando as possíveis estruturas por meio de algoritmos de busca. Além desses dois métodos, também são considerados os algoritmos híbridos, onde os dois métodos são aplicados na tarefa. A principal falha dessas abordagens tradicionais é que elas não conseguem identificar todas as relações existentes nos dados, sendo necessário investigar novas abordagem. Desta forma, esta pesquisa apresenta o desenvolvimento de uma métrica fuzzy de avaliação com um método de busca heurística para aprendizagem de estrutura de redes bayesianas, utilizando Monte Carlo via Cadeias de Markov. As diferentes métricas de avaliação de redes bayesianas utilizadas permitem identificar determinadas propriedades nas redes. Essas propriedades são determinadas em função da métrica aplicada. A combinação em uma métrica fuzzy possibilita avaliar diferentes propriedades simultaneamente. Os resultados deste trabalho foram avaliados no contexto de bases sintéticas por meio da comparação com outros algoritmos, convergência das cadeias de Markov e tempo de processamento. Os resultados evidenciam, apesar do tempo de processamento, que a métrica proposta, além de compatível com os algoritmos clássicos, melhorou o processo de avaliação de estruturas combinando diferentes métricas em uma métrica fuzzy.Abstract : Learning bayesian networks (BN) from data is considered a complex task, since the number of possible structures grows exponentially with the number of variables. There are two main approaches for learning BN: methods based on independence tests, seeking structures consistente with the tests performed on the data; methods based on heuristic search, exploring the search space with a search algorithm, evaluating the possible structures. Besides these two approaches, there are hybrid algorithms, where both methods are applied to the task. The main fault of these approaches is that they still fail to identify all existing relationships in the data, so it is necessary to investigate new approaches. This research presents the development of a fuzzy score metric in a heuristic search method for learning Bayesian network structures, in a Markov Chain Monte Carlo algorithm. Different score metrics used to learn BN structures identify certain properties in these networks. These properties are determined based on the score applied. The combination of these scores in a fuzzy metric enables the evaluation of different properties simultaneously. Results of this research were evaluated in the context of synthetic bases by comparing with other algorithms, convergence of Markov chains and processing time. The results show, despite the processing time, that the proposed metric is compatible with traditional algorithms, and improved the evaluation process of structures, combining different score metrics into a fuzzy metric

Mapping of machine learning approaches for description, prediction, and causal inference in the social and health sciences

Machine learning (ML) methodology used in the social and health sciences needs to fit the intended research purposes of description, prediction, or causal inference. This paper provides a comprehensive, systematic meta-mapping of research questions in the social and health sciences to appropriate ML approaches by incorporating the necessary requirements to statistical analysis in these disciplines. We map the established classification into description, prediction, counterfactual prediction, and causal structural learning to common research goals, such as estimating prevalence of adverse social or health outcomes, predicting the risk of an event, and identifying risk factors or causes of adverse outcomes, and explain common ML performance metrics. Such mapping may help to fully exploit the benefits of ML while considering domain-specific aspects relevant to the social and health sciences and hopefully contribute to the acceleration of the uptake of ML applications to advance both basic and applied social and health sciences research

Large-scale empirical validation of Bayesian Network structure learning algorithms with noisy data.

Numerous Bayesian Network (BN) structure learning algorithms have been proposed in the literature over the past few decades. Each publication makes an empirical or theoretical case for the algorithm proposed in that publication and results across studies are often inconsistent in their claims about which algorithm is ‘best’. This is partly because there is no agreed evaluation approach to determine their effectiveness. Moreover, each algorithm is based on a set of assumptions, such as complete data and causal sufficiency, and tend to be evaluated with data that conforms to these assumptions, however unrealistic these assumptions may be in the real world. As a result, it is widely accepted that synthetic performance overestimates real performance, although to what degree this may happen remains unknown. This paper investigates the performance of 15 state-of-the-art, well-established, or recent promising structure learning algorithms. We propose a methodology that applies the algorithms to data that incorporates synthetic noise, in an effort to better understand the performance of structure learning algorithms when applied to real data. Each algorithm is tested over multiple case studies, sample sizes, types of noise, and assessed with multiple evaluation criteria. This work involved learning approximately 10,000 graphs with a total structure learning runtime of seven months. In investigating the impact of data noise, we provide the first large scale empirical comparison of BN structure learning algorithms under different assumptions of data noise. The results suggest that traditional synthetic performance may overestimate real-world performance by anywhere between 10% and more than 50%. They also show that while score-based learning is generally superior to constraint-based learning, a higher fitting score does not necessarily imply a more accurate causal graph. The comparisons extend to other outcomes of interest, such as runtime, reliability, and resilience to noise, assessed over both small and large networks, and with both limited and big data. To facilitate comparisons with future studies, we have made all data, raw results, graphs and BN models freely available online