A novel Error-Correcting Output Codes algorithm based on genetic programming

• Published in: Swarm and evolutionary computation Vol. 50; p. 100564
• Main Authors: Li, Ke-Sen, Wang, Han-Rui, Liu, Kun-Hong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2019
• Subjects: Error-Correcting Output Codes (ECOC); Genetic operators; Genetic programming (GP); Multiclass classification; Error-Correcting Output Codes (ECOC); Genetic programming (GP); Multiclass classification; Genetic operators
• ISSN: 2210-6502
• DOI: 10.1016/j.swevo.2019.100564

Error-Correcting Output Codes (ECOC) is widely used in the field of multiclass classification. As an optimal codematrix is key to the performance of an ECOC algorithm, this paper proposes a genetic programming (GP) based ECOC algorithm (GP-ECOC). In the design of individual of our GP, each terminal node represents a class, and nonterminal nodes combine the classes in their child nodes. In this way, an individual is a class combination tree, and represents an ECOC codematrix. A legality checking process is embedded in our algorithm to check each codematrix, so as to ensure each codematrix satisfying ECOC constraints. Those violating the constraints will be corrected by a proposed Guided Mutation operator. Before fitness evaluation, a local optimization algorithm is proposed to append new columns for tough classes, so as to improve the generalization ability of each individual and accelerate the evolutionary speed. In this way, our GP can evolve optimal codematrices through the evolutionary process. Experiments show that compared with other ensemble algorithms, our algorithm can achieve stable and high performances with relatively small ensemble scales on various UCI data sets. To the best of our knowledge, it is the first time that GP has been applied to implement the ECOC encoding algorithm. Our Python code is available at https://github.com/samuellees/gpecoc. •A novel ECOC algorithm is proposed based on Genetic Programming.•Each tree-structure individual represents a solution for a multiclass problem.•A new mutation operator is designed to maintain the legality of the individual.•A local optimization algorithm tackles tough classes by adding proper columns.•A new decoding method is proposed to reduce errors in the decoding process.