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首页> 外文会议>IFIP WG 5.14 International conference on computer and computing technologies in agriculture >Feature Selection for Cotton Matter Classification
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Feature Selection for Cotton Matter Classification

机译：棉花物质分类的特征选择

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摘要

Feature selection are highly important to improve the classification accuracy of recognition systems for foreign matter in cotton. To address this problem, this paper presents six filter approaches of feature selection for obtaining the good feature combination with high classification accuracy and small size, and make comparisons using support vector machine and k-nearest neighbor classifier. The result shows that filter approach can efficiently find the good feature sets with high classification accuracy and small size, and the selected feature sets can effectively improve the performance of recognition system for foreign matter in cotton. The selected feature combination has smaller size and higher accuracy than original feature combination. It is important for developing the recognition systems for cotton matter using machine vision technology.
机译：特征选择对于提高棉花异物识别系统的分类精度非常重要。为了解决这个问题，本文提出了六种特征选择的滤波方法，以获得具有高分类精度和小尺寸的良好特征组合，并使用支持向量机和k最近邻分类器进行比较。结果表明，滤波方法可以有效地找到分类精度高，尺寸小的优良特征集，选择的特征集可以有效提高棉花异物识别系统的性能。与原始功能组合相比，所选功能组合具有更小的尺寸和更高的精度。这对于使用机器视觉技术开发棉质识别系统非常重要。

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