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The associative classification field includes really interesting approaches for building reliable classifiers and any of these approaches generally work on four different phases (data discretization, pattern mining, rule mining, and classifier building). This number of phases is a handicap when big datasets are analysed. The aim of this work is to propose a novel evolutionary algorithm for efficiently building associative classifiers in Big Data. The proposed model works in only two phases (a grammar-guided genetic programming framework is performed in each phase): 1) mining reliable association rules; 2) building an accurate classifier by ranking and combining the previously mined rules. The proposal has been implemented on Apache Spark to take advantage of the distributed computing. The experimental analysis was performend on 40 well-known datasets and considering 13 algorithms taken from literature. A series of non-parametric tests has also been carried out to determine statistical differences. Results are quite promising in terms of reliability and efficiency on high-dimensional data.

Associative Classification in Big Data through a G3P Approach