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

被引:10
作者
Yi-Fan, Liang [1 ]
Chang, Liu [1 ]
Han-Rui, Wang [1 ,2 ]
Kun-Hong, Liu [1 ]
Jun-Feng, Yao [1 ]
Ying-Ying, She [1 ]
Gui-Ming, Dai [3 ]
Okina, Yuna [3 ]
机构
[1] Xiamen Univ, Sch Informat, Xiamen 361005, Fujian, Peoples R China
[2] Fudan Univ, Sch Comp Sci, Shanghai Key Lab Intelligent Informat Proc, Shanghai, Peoples R China
[3] Sumitomo Elect Ind Ltd, R&D Unit, Frontier Technol Lab, 1 Taya Cho, Yokohama, Kanagawa 2448588, Japan
基金
中国国家自然科学基金; 国家重点研发计划;
关键词
Error-correcting output code; Ternary digit operator; Genetic programming; Feature selection; CLASSIFICATION; ECOC; SELECTION; ENSEMBLE; DESIGN;
D O I
10.1016/j.patcog.2020.107642
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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. (c) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页数:19
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