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ENHANCING THE CLASSIFICATION ACCURACY OF NOISY DATASET BY FUSING CORRELATION BASED FEATURE SELECTION WITH K-NEAREST NEIGHBOUR

机译：通过熔断基于邻居的相关的特征选择来提高噪声数据集的分类准确性

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Samir Singha; Syed Hassan;
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年度 2017
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1. Enhancing the Classification Accuracy of Noisy Dataset by Fusing Correlation Based Feature Selection With K-Nearest Neighbour [J] . SAMIR KUMAR SINGHA, SYED IMTIAZ HASSAN Oriental Journal of Computer Science and Technology . 2017,第2期

机译：通过将基于相关的特征选择与K最近邻融合来提高噪声数据集的分类精度
2. Enhancing the Classification Accuracy of Noisy Dataset By Fusing Correlation Based Feature Selection with K-Nearest Neighbour [J] . Samir Kumar Singha, Syed Imtiaz Hassan Oriental journal of computer science and technology . 2017,第2期

机译：通过将基于相关的特征选择与K最近邻融合来提高噪声数据集的分类精度
3. Feature Selection and the Fusion-based Method for Enhancing the Classification Accuracy of SVM for Breast Cancer Detection [J] . Ali Ahmed, Sharaf J.Malebary International journal of computer science and network security . 2019,第11期

机译：用于增强SVM分类精度的特征选择和基于融合的方法
4. Accuracy Enhancement of Correlated Naive Bayes Method by Using Correlation Feature Selection (CFS) for Health Data Classification [C] . Hairani Hairani, Muhammad Innuddin, Majid Rahardi International Conference on Information and Communications Technology . 2020

机译：使用相关特征选择（CFS）进行健康数据分类的相关特征选择（CFS）的准确性提高
5. Comparative classification of prostate cancer data using the Support Vector Machine, Random Forest, DualKS and k-Nearest Neighbours. [D] . Sakouvogui, Kekoura. 2015

机译：使用支持向量机，Random Forest，DualKS和k-Nearest邻居对前列腺癌数据进行比较分类。
6. Love Thy Neighbour: Automatic Animal Behavioural Classification of Acceleration Data Using the K-Nearest Neighbour Algorithm [O] . Owen R. Bidder, Hamish A. Campbell, Agustina Gómez-Laich, 2010

机译：爱你的邻居：使用K最近邻居算法对加速度数据进行自动动物行为分类
7. An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons [O] . Md Akizur Rahman, Ravie Chandren Muniyandi 2020

机译：一种基于15神经元的人工神经网络的两步特征选择方法提高癌症分类精度

ENHANCING THE CLASSIFICATION ACCURACY OF NOISY DATASET BY FUSING CORRELATION BASED FEATURE SELECTION WITH K-NEAREST NEIGHBOUR

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