This paper proposes a novel genetic algorithm (GA) for the error correction output coding (ECOC) framework. Different from other GA algorithms, a new individual structure is designed by setting a gene as the combination of three types of operators: (1) the column selector; (2) the ternary bitwise calculator; (3) the feature selector. In our GA algorithm, two column selectors first extract two columns to from a codematrix pool, then a Ternary bitwise Calculator (TC) transfers them to a new column through a ternary number calculation process. The feature selector selects a feature subset for training the associated dichotomizer. By doing so, an individual contains a set of genes to form an ECOC ensemble. The TCs deployed in our algorithm include both the traditional TCs and some newly proposed TCs, which aid to generate diverse codematrices. When the evolutionary process terminates, the best individual in the last generation is regarded as the final solution. The performance of our algorithm is verified on both the UCI and microarray data sets. Experiment results demonstrate that our GA based ECOC achieves promising performance comparing to other ECOC algorithms. Furthermore, results also confirm that various TCs contribute to the generation of discriminative individuals.

本文提出了一种用于错误校正输出编码（ECOC）框架的新型遗传算法（GA）。与其他GA算法不同，通过将基因设置为三种类型的运算符的组合来设计新的单个结构：（1）列选择器；（2）三进制按位计算器；（3）功能选择器。在我们的GA算法中，两个列选择器首先从一个代码矩阵池中提取两列，然后通过三进制按位计算器（TC）通过三进制数计算过程将它们转移到新列中。特征选择器选择特征子集以训练相关的二分法器。通过这样做，一个人包含一组基因以形成ECOC集合。我们算法中部署的TC既包括传统TC，又包括一些新提出的TC，它们有助于生成各种码矩阵。当进化过程终止时，最后一代中的最佳个体被视为最终解决方案。我们的算法的性能已在UCI和微阵列数据集上得到验证。实验结果表明，与其他ECOC算法相比，我们基于GA的ECOC取得了令人鼓舞的性能。此外，结果还证实了各种TC有助于产生歧视性个体。