Korns, Michael F. “An Evolutionary Algorithm for Big Data Multi-Class Classification Problems.” In Genetic Programming Theory and Practice XIV , pp. 165-178. Springer, Cham, 2018.
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As symbolic regression (SR) has advanced into the early stages of commercial exploitation, the poor accuracy of SR still plagues even advanced commercial packages, and has become an issue for industrial users. Users expect a correct formula to be returned, especially in cases with zero noise and only one basis function with minimal complexity. At a minimum, users expect the response surface of the SR tool to be easily understood, so that the user can know a priori on what classes of problems to expect excellent, average, or poor accuracy. Poor or unknown accuracy is a hindrance to greater academic and industrial acceptance of SR tools. In several previous papers, we presented a complex algorithm for modern SR, which is extremely accurate for a large class of SR problems on noiseless data. Further research has shown that these extremely accurate SR algorithms also improve accuracy in noisy circumstances—albeit not extreme accuracy. Armed with these SR successes, we naively thought that achieving extreme accuracy applying GP to symbolic multi-class classification would be an easy goal. However, it seems algorithms having extreme accuracy in SR do not translate directly into symbolic multi-class classification. Furthermore, others have encountered serious issues applying GP to symbolic multi-class classification (Castelli et al. Applications of Evolutionary Computing, EvoApplications 2013: EvoCOMNET, EvoCOMPLEX, EvoENERGY, EvoFIN, EvoGAMES, EvoIASP, EvoINDUSTRY, EvoNUM, EvoPAR, EvoRISK, EvoROBOT, EvoSTOC, vol 7835, pp 334–343. Springer, Vienna, 2013). This is the first paper in a planned series developing the necessary algorithms for extreme accuracy in GP applied to symbolic multi-class classification. We develop an evolutionary algorithm for optimizing a single symbolic multi-class classification candidate. It is designed for big-data situations where the computational effort grows linearly as the number of features and training points increase. The algorithm’s behavior is demonstrated on theoretical problems, UCI benchmarks, and industry test cases.