Rule Extraction on Numeric Datasets Using Hyper-rectangles

Waldo Hasperué, Laura Cristina Lanzarini, Armando De Giusti

Abstract


When there is a need to understand the data stored in a database, one of the main requirements is being able to extract knowledge in the form of rules. Classification strategies allow extracting rules almost naturally. In this paper, a new classification strategy is presented that uses hyper-rectangles as data descriptors to achieve a model that allows extracting knowledge in the form of classification rules. The participation of an expert for training the model is discussed. Finally, the results obtained using the databases from the UCI repository are presented and compared with other existing classification models, showing that the algorithm presented requires less computational resources and achieves the same accuracy level and number of extracted rules.


Full Text: PDF DOI: 10.5539/cis.v5n4p116

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This work is licensed under a Creative Commons Attribution 3.0 License.

Computer and Information Science   ISSN 1913-8989 (Print)   ISSN 1913-8997 (Online)
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