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Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

机译：韩国食品安全推文意图分类的机器学习分类器及特征集选择研究

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Yeom, Ha-Neul; Hwang, Myunggwon; Hwang, Mi-Nyeong; Jung, Hanmin;
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年度 2014
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1. Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety [J] . Ha-Neul Yeom, Myunggwon Hwang, Mi-Nyeong Hwang, Journal of Information Science Theory and Practice . 2014,第3期

机译：关于食品安全的韩国推文意图分类的机器学习分类器和特征集选择研究
2. Comparison of radiomics machine-learning classifiers and feature selection for differentiation of sacral chordoma and sacral giant cell tumour based on 3D computed tomography features [J] . Yin Ping, Mao Ning, Zhao Chao, European radiology . 2019,第4期

机译：基于3D计算机断层扫描特征的骶瘤机学习分类器和特征选择的比较骶骨脊髓瘤和骶巨细胞瘤
3. Rough set theory-based feature selection and FGA-NN classifier for medical data classification [J] . Vijayalakshmi Balasubramanian, Sugumar Rajendran International Journal of Business Intelligence and Data Mining . 2019,第3期

机译：基于粗糙集理论的特征选择和FGA-NN分类器进行医学数据分类
4. Tweet Sentiment Classification Using an Ensemble of Machine Learning Supervised Classifiers Employing Statistical Feature Selection Methods [C] . K. Lakshmi Devi, P. Subathra, P.N. Kumar International Conference on Fuzzy and Neuro Computing . 2015

机译：推文情绪分类使用机器学习的集合监督分类器采用统计特征选择方法
5. Feature Set Selection for Improved Classification of Static Analysis Alerts [D] . ?Goeschel, Kathleen 2019

机译：功能设置选择，以改进静态分析警报分类
6. Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection [O] . Lal Hussain, Tony Nguyen, Haifang Li, 2020

机译：便携式胸部X射线纹理特征的机器学习分类准确分类Covid-19肺部感染
7. Table 5: Accuracy of SVM classifier on DBAP features derived from pre-trained LeNet with DBAP layer. The DBAP features show better classification results than the MaxPool features in LeNet. The fully connected (FC) layers of LeNet with DBAP also tend to show better discrimination ability as compared to FC layer features extracted from regular LeNet on all benchmark data sets. [O] . -1

机译：表5：SVM分类器对具有DBAP层的预先训练的Lenet派生的DBAP特征的精度。 DBAP功能显示比LENET中的MAXPOOL功能更好的分类结果。与DBAP的完全连接（FC）LENET的层也倾向于显示与在所有基准数据集上从常规LENET提取的FC层特征相比显示出更好的辨别能力。

Study of Machine-Learning Classifier and Feature Set Selection for Intent Classification of Korean Tweets about Food Safety

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