Abstract
Traditional machine learning systems learn from non-relational data but in fact most of the real world data is relational. Normally the learning task is done using a single flat file, which prevents the discovery of effective relations among records. Inductive logic programming and statistical relational learning partially solve this problem. In this work, we resource to another method to overcome this problem and propose the T-SPPA: Trended Statistical PreProcessing Algorithm, a preprocessing method that translates related records to one single record before learning. Using different kinds of data, we compare our results when learning with the transformed data with results produced when learning from the original data to demonstrate the efficacy of our method.
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Silva, T., Dutra, I. (2011). T-SPPA: Trended Statistical PreProcessing Algorithm. In: Snasel, V., Platos, J., El-Qawasmeh, E. (eds) Digital Information Processing and Communications. ICDIPC 2011. Communications in Computer and Information Science, vol 188. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22389-1_11
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DOI: https://doi.org/10.1007/978-3-642-22389-1_11
Publisher Name: Springer, Berlin, Heidelberg
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