基于标签关联性的多标签Scratch分类算法

为了实现Scratch可视化编程领域的作品分类，提出了一种基于标签关联性的多标签分类算法（MLLR），构建了一个有效的多标签Scratch分类模型.首先提取作品的Block使用特征、计算思维技能特征和复杂度特征3类特征作为分类特征；然后针对RAKEL算法随机选择标签子集，忽略了标签间的关联性，提出了改进的MLLR算法，该方法根据多标签之间的关联性来划分标签子集，再训练相应的标签幂集子分类器.实验结果表明，MLLR算法在分类性能和时间性能上优于RAKEL等多标签分类算法，构建的分类模型对于Scratch作品具有较强的适用性，分类的准确率达到81.3%.

Label Relevance Based Multi-Label Scratch Classification Algorithm

In order to implement the classification of projects in visual programming field of Scratch, a multi-label classification algorithm (MLLR) appears based on label relevance. An effective multi-label classification model for Scratch projects was constructed. Firstly, the block usage features, the computational thinking skill features and the Halstead features of projects are extracted as classification features. Then, the RAKEL algorithm randomly chooses label subsets, ignoring the relevance between labels, thereafter an improved MLLR algorithm was proposed. This method divides label subsets according to the relevance between multiple labels, and then trains the corresponding label power set sub-classifiers. Experiments show that MLLR algorithm is superior to RAKEL and other multi-label classification algorithms in classification performance and time performance, The classification model constructed has a strong applicability for Scratch projects, and the accuracy of classification reaches 81.3%.