Abstract This paper introduces the genetic algorithm-adaptive neuro-fuzzy inference system (GA-ANFIS), as a novel nonlinear feature selection method. This hybrid technique combines genetic algorithms (GAs) as powerful optimization methods with ANFIS as a robust nonlinear statistical method. Multivariate image analysis whose descriptors achieved from bidimensional images coupled to principal component analysis (PCA) and the most significant principal components (PCs) were extracted. Eigen value ranking (EV), correlation ranking (CR), GA-partial least square (GA-PLS) as well the method proposed in this work were used to select the most relevant set of PCs as inputs for ANFIS to assess electron capture detector responses of 207 polychlorinated biphenyls. The results indicated that GA-ANFIS is superior over the others in both selecting the most relevant set of PCs and correlating the inputs (PCs) with the detector responses. The best model was statistically validated for its predictive power using cross-validation, applicability domain and Y-scrambling evaluation procedures. Moreover, the superiority of this model obtained from pixels of chemical structures over the nonlinear one obtained from original molecular descriptors in a previous work indicates that the image analysis is a powerful tool in quantitative structure-(chromatographic) property relationship (QSPR) studies.

中文翻译：

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使用遗传算法自适应神经模糊推理系统作为一种新型非线性特征选择方法，化学结构的像素与色谱检测器响应相关

摘要 本文介绍了遗传算法自适应神经模糊推理系统（GA-ANFIS），作为一种新的非线性特征选择方法。这种混合技术将遗传算法 (GA) 作为强大的优化方法与作为稳健非线性统计方法的 ANFIS 相结合。多变量图像分析，其描述符从二维图像中获得，耦合到主成分分析 (PCA) 和最重要的主成分 (PC)。特征值排序 (EV)、相关排序 (CR)、GA 偏最小二乘 (GA-PLS) 以及本工作中提出的方法用于选择最相关的 PC 集作为 ANFIS 的输入，以评估电子捕获探测器207 种多氯联苯的响应。结果表明 GA-ANFIS 在选择最相关的 PC 集和将输入 (PC) 与检测器响应相关联方面优于其他方法。使用交叉验证、适用性域和 Y 加扰评估程序对最佳模型的预测能力进行了统计验证。此外，该模型从化学结构的像素获得的模型优于从先前工作中的原始分子描述符获得的非线性模型，这表明图像分析是定量结构（色谱）特性关系（QSPR）研究中的强大工具。