4.7 Article

Research on Apple Origins Classification Optimization Based on Least-Angle Regression in Instance Selection

期刊

AGRICULTURE-BASEL
卷 13, 期 10, 页码 -

出版社

MDPI
DOI: 10.3390/agriculture13101868

关键词

instance selection; least-angle regression; classification; SVM; near-infrared spectroscopy

类别

向作者/读者索取更多资源

Machine learning is widely used in near-infrared spectroscopy (NIRS) for fruit classification. A classification instance selection method based on the least-angle regression (LAR) was proposed to compress the sample size while improving accuracy, and experimental results supported its effectiveness.
Machine learning is used widely in near-infrared spectroscopy (NIRS) for fruit qualification. However, the directly split training set used contains redundant samples, and errors may be introduced into the model. Euclidean distance-based and K-nearest neighbor-based instance selection (IS) methods are widely used to remove useless samples because of their accessibility. However, they either have high accuracy and low compression or vice versa. To compress the sample size while improving the accuracy, the least-angle regression (LAR) method was proposed for classification instance selection, and a discrimination experiment was conducted on a total of four origins of 952 apples. The sample sets were split into the raw training set and testing set; the optimal training samples were selected using the LAR-based instance selection (LARIS) method, and the four other selection methods were compared. The results showed that 26.9% of the raw training samples were selected using LARIS, and the model based on these training samples had the highest accuracy. Thus, the apple origin classification model based on LARIS can achieve the goal of high accuracy and compression and provide experimental support for the least-angle regression algorithm in classification instance selection.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据