A novel error-correcting output codes based on genetic programming and ternary digit operators

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Article{YIFAN:2021:PR,

• author = "Liang Yi-Fan and Liu Chang and Wang Han-Rui and Liu Kun-Hong and Yao Jun-Feng and She Ying-Ying and Dai Gui-Ming and Yuna Okina",
• title = "A novel error-correcting output codes based on genetic programming and ternary digit operators",
• journal = "Pattern Recognition",
• volume = "110",
• pages = "107642",
• year = "2021",
• ISSN = "0031-3203",
• DOI = "doi:10.1016/j.patcog.2020.107642",
• URL = "https://www.sciencedirect.com/science/article/pii/S0031320320304453",
• keywords = "genetic algorithms, genetic programming, Error-correcting output code, Ternary digit operator, Feature selection",
• abstract = "The key to the success of an Error-Correcting Output Code (ECOC) algorithm is the effective codematrix, which represents a set of class reassignment schemes for decomposing a multiclass problem into a set of binary class problems. This paper proposes a new method, which uses Ternary digit Operators based Genetic Programming (GP) to generate effective ECOC codematrix (TOGP-ECOC for short). In our GP, each terminal node stores a ternary digit string, representing a column and a related feature subset; each non-terminal node represents a ternary digit operator, which produces a new column based on its child nodes. In this way, each individual is interpreted as an ECOC codematrix along with a set of corresponding feature subsets, serving the solution for the multiclass classification task. When a new individual is produced, a legality checking process is carried out to verify whether the transformed codematrix follows the ECOC constraints. The illegal one is corrected according to different strategies. Besides, a local optimization algorithm is designed to prune redundant columns and improve the performance of each individual. Our experiments compared TOGP-ECOC with some well known ECOC algorithms on various data sets, and the results confirm the superiority of our algorithm. Our source code is available at: https://github.com/MLDMXM2017/TOGP-ECOC",

}

Genetic Programming entries for Liang Yi-Fan Liu Chang Wang Han-Rui Liu Kun-Hong Yao Jun-Feng She Ying-Ying Dai Gui-Ming Yuna Okina

Citations