关键词: 多标号算法, 学习矢量量化算法, 元标号分类器, 高效液相色谱法, 食用油掺伪检测

Abstract: To improve the detection effect in oil adulteration, a new algorithm called ML-LVQ (Multi-Label Learning Vector Quantization) was proposed, which adapted Learning Vector Quantization (LVQ) to solve the multi-label learning problem on High Performance Liquid Chromatography (HPLC) data. It could minimize the upper bound of the ranking error, which would benefit the ranking measure. Moreover, the meta-labeler was used to identify the number of the labels for improving the bipartitions measure. The experimental results on nine classes of pure oil and their mixed oil samples show that the proposed algorithm is superior to the improved AdaBoost.RMH.

Key words: multi-label algorithm, Learning Vector Quantization (LVQ) algorithm, meta-labeler, High Performance Liquid Chromatography (HPLC) method, oil adulteration detection