Hierarchical Multi-Label Classification of Library Subject Headings

Assigning the subject heading is a significant work of the librarian. Subject heading represents the overall contents of the books which is a great way to easily find the books related directly to the user's needs. But a book can be related to more than one subject and Some librarians are not experts in every discipline. They take more time on this process. In this paper, our research has a small dataset, so the multi-label classification technique is unsuitable for this work. Therefore, we propose the hierarchical multi-label classification to help them assign subject heading to the book from a title and a table of contents. We also compare the performance of three techniques: Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN), to select the best classification technique. According to the results, Random Forest technique is the best one for the first hierarchy classifiers with the highest F1 of 0.908. The second hierarchy classifiers that uses Random Forest technique attains the F1 score of 0.871.