Multilabel associative classification categorization of MEDLINE articles into MeSH keywords.
The specific characteristic of classification of medical documents from the MEDLINE database is that each document is assigned to more than one category, which requires a system for multilabel classification. Another major challenge was to develop a scalable method capable of dealing with hundreds of thousand of documents. We proposed a novel system for automated classification of MEDLINE documents to MeSH keywords based on the recently developed data mining algorithm called ACRI, which was modified to accommodate multilabel classification. Five different classification configurations in conjunction with different methods of measuring classification quality were proposed and tested. The extensive experimental comparison showed superiority of methods based on reoccurrence of words in an article over nonrecurrent-based associative classification. The achieved relatively high value of macro F1 (46%) demonstrates the high quality of the proposed system for this challenging dataset. Accuracy of the proposed classifier, defined as the ratio of the sum of TP and TN examples to the total number of examples, reached 90%. Three scenarios were proposed based on the performed tests and different possible objectives. If a goal is to classify the largest number of documents, a configuration that maximizes micro F1 should be chosen. On the other hand, if a system is to work well for categories with a small number of documents, a configuration that maximizes macro F1 is more suitable. A tradeoff can be obtained by using a configuration that optimizes the average between macro and micro F1.
Rak
Rafal
R
Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. rrak@ece.ualberta.ca
Kurgan
Lukasz A
LA
Reformat
Marek
M
D000043:Abstracting and Indexing / Q000379:methods*; D001185:Artificial Intelligence; D016247:Information Storage and Retrieval / Q000379:methods; D016239:MEDLINE; D046650:Medical Subject Headings; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D010506:Periodicals as Topic / Q000145:classification*; D009626:Terminology as Topic