Abstract: 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%.

Key words: Scratch, label relevance, multi-label classification, classification model