MapReduce‐based parallel GEP algorithm for efficient function mining in big data applications

摘要

Gene expression programming (GEP) algorithm is one of the most effective function mining algorithmsin enabling the mathematical equation fitting for the input dataset. However, GEP algorithmencounters low efficiency issue in big data processing due to large overhead in itsevolution when it handles the large‐scale data. In order to solve the issue, this paper presentstwo parallelized GEP algorithms using MapReduce. Based on data separation, the first algorithmaims at speeding up the large‐scale classification. However, it is lack of ability to output the minedequation explicitly. Therefore, based on the further improvements of the first algorithm, the secondparallelized GEP algorithm aims at mining the equation efficiently and also outputs the equationexplicitly and directly. The experimental results show that both algorithms are effective forprocessing large volume of data.