Confusion Matrices for Classification Algorithms of Raw Fiber, Yarn, and Fabric Samples

Confussion matrices from 3 studies where different classification algorithms were employed including PLS-DA, K-NN, SVM, SIMCA and ANN. Studies used raw fiber, yarn and fabric samples.Also, a detailed description of five classification models for Near Infrared Spectroscopy is presented in Word format. Algorithms described are:-Partial Least Squares Discriminant Analysis (PLS-DA)-K-Nearest Neighbours (K-NN)-Smooth Independent Modeling with Class Analogy (SIMCA)-Support Vector Machine (SVM)-Artificial Neural Networks (ANN)本数据集包含3项研究生成的混淆矩阵，这些研究采用了多种分类算法，包括偏最小二乘判别分析（Partial Least Squares Discriminant Analysis, PLS-DA）、K近邻（K-Nearest Neighbours, K-NN）、支持向量机（Support Vector Machine, SVM）、平滑类别类比独立建模（Smooth Independent Modeling with Class Analogy, SIMCA）以及人工神经网络（Artificial Neural Networks, ANN）。上述研究均使用了原始纤维、纱线与织物样品。 此外，本数据集还以Word格式附带了针对近红外光谱（Near Infrared Spectroscopy）的5种分类模型的详细说明，所涉及的算法具体如下： - 偏最小二乘判别分析（PLS-DA） - K近邻（K-NN） - 平滑类别类比独立建模（SIMCA） - 支持向量机（SVM） - 人工神经网络（ANN）