Precision And Comprehensibility In The Integration Of Regression Rules
Author(s)
J. B. Pugliesi & S. O. Rezende
Abstract
Precision and comprehensibility in the
integration of regression rules
J. B. Pugliesi & S. 0. Rezende
Institute of Mathematics and Computer Sciences-ICMC,
University of Siio Paulo-US8 Siio Carlos, Brazil
Abstract
Data Mining is the process of data analysis and application of algorithms that are
able to find a particular pattern relationship from a large amount of data. Regression
is an important problem in data analysis and appears in many real world applications.
Therefore, there is an increasing interest in the use of the Data Mining to
extract patterns from regression problems. The main objective of this paper is to
support the users of Data Mining process in the evaluation of integrated knowledge
expressed in the form of regression rules. To explore the knowledge evaluation,
experiments are executed with Cubist, RT and M5 algorithms. The 10-fold crossvalidation
is used to find the mean error. All regression rules are transformed to a
standard syntax and put together in a unique set of rules
Keywords
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