کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
379444 | 659302 | 2007 | 17 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Strategies for improving the modeling and interpretability of Bayesian networks
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
One of the main factors for the knowledge discovery success is related to the comprehensibility of the patterns discovered by applying data mining techniques. Amongst which we can point out the Bayesian networks as one of the most prominent when considering the easiness of knowledge interpretation achieved. Bayesian networks, however, present limitations and disadvantages regarding their use and applicability. This paper presents an extension for the improvement of Bayesian networks, treating aspects such as performance, as well as interpretability and use of their results; incorporating genetic algorithms in the model, multivariate regression for structure learning and temporal aspects using Markov chains.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Data & Knowledge Engineering - Volume 63, Issue 1, October 2007, Pages 91-107
Journal: Data & Knowledge Engineering - Volume 63, Issue 1, October 2007, Pages 91-107
نویسندگان
Ádamo L. de Santana, Carlos R. Francês, Cláudio A. Rocha, Solon V. Carvalho, Nandamudi L. Vijaykumar, Liviane P. Rego, João C. Costa,