模式识别(心理学)
人工智能
降维
特征选择
随机森林
支持向量机
自编码
线性判别分析
维数之咒
计算机科学
朴素贝叶斯分类器
数学
人工神经网络
作者
Edward Harefa,Weidong Zhou
出处
期刊:Analytical Methods
[The Royal Society of Chemistry]
日期:2021-01-01
卷期号:13 (41): 4926-4933
被引量:7
摘要
The feasibility and accuracy of several combination classification models, i.e., quadratic discriminant analysis (QDA), random forest (RF), Bernoulli naïve Bayes (BNB), and support vector machine (SVM) classification models combined with either sequential feature selection (SFS) or dimensionality reduction methods, for classifying soil with laser-induced breakdown spectroscopy (LIBS) had been explored in this study. Each algorithm combination was compared to assess their classification performance. After eliminating the irrelevant features of the data using sequential feature selection (SFS), the performances were all improved for the studied four classification models, and the best accuracy reached 97.88% by SFS-SVM. The dimensions of the data were then reduced using variational autoencoder (VAE), truncated singular value decomposition (TSVD), and isometric mapping (Isomap), respectively. The classification accuracy improved for all combination models with dimensionality reduction, and impressive accuracies of 98.12% from TSVD-SVM and 98.24% from VAE-SVM were obtained. These results demonstrate an effective way to reduce uncorrelated features, high dimensionality, and redundant information in the LIBS dataset. In addition, coupling classification models with feature selection and dimensionality reduction techniques could significantly optimize the classification performance of LIBS.
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