کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
396982 1438451 2014 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Learning continuous time Bayesian network classifiers
ترجمه فارسی عنوان
مدرک مداد کلاسهای بیزی را یاد بگیرید
کلمات کلیدی
داده های جریان، مسیر چند متغیره، طبقه بندی مداوم شبکه های پیوسته بیزی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی


• New models from the class of continuous time Bayesian network classifiers (CTBNCs).
• Derivation of conditional log-likelihood scoring function for CTBNCs.
• Algorithm, based on conditional log-likelihood score function, for learning CTBNCs.
• Performance comparison between DBNs and CTBNCs learned with different score functions.
• Performance analysis of CTBNCs on the problem of post-stroke rehabilitation.

Streaming data are relevant to finance, computer science, and engineering while they are becoming increasingly important to medicine and biology. Continuous time Bayesian network classifiers are designed for analyzing multivariate streaming data when time duration of event matters. Structural and parametric learning for the class of continuous time Bayesian network classifiers are considered in the case where complete data is available. Conditional log-likelihood scoring is developed for structural learning on continuous time Bayesian network classifiers. Performance of continuous time Bayesian network classifiers learned when combining conditional log-likelihood scoring and Bayesian parameter estimation are compared with that achieved by continuous time Bayesian network classifiers when learning is based on marginal log-likelihood scoring and to that achieved by dynamic Bayesian network classifiers. Classifiers are compared in terms of accuracy and computation time. Comparison is based on numerical experiments where synthetic and real data are used. Results show that conditional log-likelihood scoring combined with Bayesian parameter estimation outperforms marginal log-likelihood scoring. Conditional log-likelihood scoring becomes even more effective when the amount of available data is limited. Continuous time Bayesian network classifiers outperform in terms of computation time and accuracy dynamic Bayesian network on synthetic and real data sets.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 55, Issue 8, November 2014, Pages 1728–1746
نویسندگان
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