Structural Evolutionary Learning for Composite Classification Models
Nikolay O. Nikitin, Iana S. Polonskaia, Pavel Vychuzhanin, Irina V. Barabanova, Anna V. Kalyuzhnaya
Abstract
In this paper, we propose an evolutionary learning approach for flexible identification of custom composite models for classification problems. To solve this problem in an efficient way, the problem-specific evolutionary operators are proposed and the effectiveness of different modifications of the common genetic programming algorithm is investigated. Also, several implementations of caching for the fitted models were compared from the performance point of view. To verify the proposed algorithm, both synthetic and real-world classification cases are examined. The implemented solution can identify the structure of the composite models from scratch, as well as be used as a part of automated machine learning solutions.