Strategic approach for Multiple-MLP Ensemble Re-RX algorithm

Abstract

In this paper, we review all our work since 2012 and propose a strategic approach for the Multiple-MLP Ensemble Re- RX algorithm. We first describe the background and procedures of the Recursive-Rule Extraction (Re-RX) algorithm family and its variants, including the Multiple-MLP Ensemble Re-RX algorithm (“Multiple-MLP Ensemble”), which uses the Re-RX algorithm as its core. The proposed strategic approach consists of two processes: non-pruning for the trained neural network ensembles without continuous attributes and a relaxed rule generation scheme using continuous attributes to extract extremely accurate, comprehensible, and concise rules for multi-class mixed datasets (i.e., discrete attributes and continuous attributes). We conducted experiments to find rules for seven kinds of multi-class mixed datasets and compared the accuracy, comprehensibility, and conciseness for the Multiple-MLP Ensemble Re-RX algorithm. The strategic approach for the Multiple-MLP Ensemble Re-RX algorithm outperformed the original Multiple-MLP Ensemble Re- RX algorithm. These results confirm that the strategic approach for the Multiple-MLP Ensemble algorithm facilitates the migration from existing data systems toward new accurate analytic systems and Big Data.