A Statistical Implicative Analysis Based Algorithm and MMPC Algorithm for Detecting Multiple Dependencies

Discovering the dependencies among the variables of a domain from examples is an important problem in optimization. Many methods have been proposed for this purpose, but few large-scale evaluations were conducted. Most of these methods are based on measurements of conditional probability. The statistical implicative analysis offers another perspective of dependencies. It is important to compare the results obtained using this approach with one of the best methods currently available for this task: the MMPC heuristic. As the SIA is not used directly to address this problem, we designed an extension of it for our purpose. We conducted a large number of experiments by varying parameters such as the number of dependencies, the number of variables involved or the type of their distribution to compare the two approaches. The results show strong complementarities of the two methods.

• Type: Journal
• Year: 2010
• Where: JMLR
• Authors: Elham Salehi, Jayashree Nyayachavadi, Robin Gras
• Keywords: JMLR 2010, Large-scale Evaluations, Many Methods, Statistical Implicative Analysis