کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9952095 | 1438402 | 2018 | 12 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
On scoring Maximal Ancestral Graphs with the Max-Min Hill Climbing algorithm
ترجمه فارسی عنوان
در برآورد حداکثر نمودار اجدادی با الگوریتم صعود حداکثر حداقل ارتفاع
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
We consider the problem of causal structure learning in presence of latent confounders. We propose a hybrid method, MAG Max-Min Hill-Climbing (M3HC) that takes as input a data set of continuous variables, assumed to follow a multivariate Gaussian distribution, and outputs the best fitting maximal ancestral graph. M3HC builds upon a previously proposed method, namely GSMAG, by introducing a constraint-based first phase that greatly reduces the space of structures to investigate. On a large scale experimentation we show that the proposed algorithm greatly improves on GSMAG in all comparisons, and over a set of known networks from the literature it compares positively against FCI and cFCI as well as competitively against GFCI, three well known constraint-based approaches for causal-network reconstruction in presence of latent confounders.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 102, November 2018, Pages 74-85
Journal: International Journal of Approximate Reasoning - Volume 102, November 2018, Pages 74-85
نویسندگان
Konstantinos Tsirlis, Vincenzo Lagani, Sofia Triantafillou, Ioannis Tsamardinos,