A Method of Feature Selection for Textile Yarn Grading Using the Effective Distance Between Clusters

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Abstract
By using the effective distance between clusters (EDC) as the basis for feature selection, this paper achieves a significant and effective feature for textile yarn grading, and further upgrades the operational efficiency of such grading. The results, such as feature selection processing to principal axis vectors (PAVS) by EDC, show that the feature's average number and average total distance of mistaken ranking by EDC are only 33.3% and 16.7% of those by Karhunen-Loeve (K-L) expansion, respectively. Furthermore, EDC can be applied directly to the feature selection of property vectors (PVS) and can reduce the measured items of pvs without lowering identification precision. Compared with our previous method of textile yarn grading, EDC provides 16.7% greater efficiency both in measuring pvs and calculating PAV1 time.

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