A New Bayesian Tree Learning Method with Reduced Time and Space Complexity

• Autorzy: Kłopotek, M.A.
• Języki publikacji: EN

Abstrakty

EN

Bayesian networks have many practical applications due to their capability to represent joint probability distribution in many variables in a compact way. There exist efficient reasoning methods for Bayesian networks. Many algorithms for learning Bayesian networks from empirical data have been developed. A well-known problem with Bayesian networks is the practical limitation for the number of variables for which a Bayesian network can be learned in reasonable time. A remarkable exception here is the Chow/Liu algorithm for learning tree-like Bayesian networks. However, its quadratic time and space complexity in the number of variables may prove also prohibitive for high dimensional data. The paper presents a novel algorithm overcoming this limitation for the tree-like class of Bayesian networks. The new algorithm space consumption grows linearly with the number of variables n while the execution time is proportional to nźln(n), hence both are better than those of Chow/Liu algorithm. This opens new perspectives in construction of Bayesian networks from data containing tens of thousands and more variables, e.g. in automatic text categorization.

Słowa kluczowe

EN

machine learning; Bayesian networks; Chow/Liu algorithm; time complexity; space complexity

• Czasopismo: Fundamenta Informaticae
• Rocznik: 2002
• Tom: Vol. 49, nr 4
• Strony: 349-367
• Opis fizyczny: wykr., bibliogr. 16 poz.

Twórcy

autor

Kłopotek, M.A.

• Institute of Computer Science, Polish Academy of Science, ul. Ordona 21, 01-237 Warszawa, Poland,