Ensemble learning combines multiple learners to perform combinatorial learning, which has advantages of good flexibility and higher generalization performance. To achieve higher quality cancer classification, in this study, the fast correlation-based feature selection (FCBF) method was used to preprocess the data to eliminate irrelevant and redundant features. Then, the classification was carried out in the stacking ensemble learner. A library for support vector machine (LIBSVM), -nearest neighbor (KNN), decision tree C4.5 (C4.5), and random forest (RF) were used as the primary learners of the stacking ensemble. Given the imbalanced characteristics of cancer gene expression data, the embedding cost-sensitive naive Bayes was used as the metalearner of the stacking ensemble, which was represented as CSNB stacking. The proposed CSNB stacking method was applied to nine cancer datasets to further verify the classification performance of the model. Compared with other classification methods, such as single classifier algorithms and ensemble algorithms, the experimental results showed the effectiveness and robustness of the proposed method in processing different types of cancer data. This method may therefore help guide cancer diagnosis and research.

中文翻译：

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使用成本敏感的朴素贝叶斯堆叠集成进行癌症分类


集成学习将多个学习器结合起来进行组合学习，具有灵活性好、泛化性能高等优点。为了实现更高质量的癌症分类，本研究采用基于快速相关的特征选择（FCBF）方法对数据进行预处理，消除不相关和冗余的特征。然后，在堆叠集成学习器中进行分类。支持向量机 (LIBSVM) 库， -最近邻（KNN）、决策树C4.5（C4.5）和随机森林（RF）被用作堆叠集成的主要学习器。考虑到癌症基因表达数据的不平衡特征，采用嵌入成本敏感的朴素贝叶斯作为堆叠集成的元学习器，表示为CSNB堆叠。将所提出的CSNB堆叠方法应用于九个癌症数据集，以进一步验证模型的分类性能。与其他分类方法（例如单分类器算法和集成算法）相比，实验结果表明了该方法在处理不同类型癌症数据时的有效性和鲁棒性。因此，这种方法可能有助于指导癌症诊断和研究。