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Classifying Lung Cancer Knowledge in PubMed According to GO Terms Using Extreme Learning Machine
Author(s) -
Sun Xia,
Xu Xuebin,
Wang Jiarong,
Feng Jun,
Chen SuShing
Publication year - 2014
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21675
Subject(s) - computer science , ontology , extreme learning machine , artificial intelligence , digital library , scheme (mathematics) , knowledge base , task (project management) , machine learning , information retrieval , artificial neural network , mathematics , art , mathematical analysis , philosophy , literature , poetry , epistemology , management , economics
For a well‐established digital library (e.g., PubMed), searching in terms of a newly established ontology (e.g., Gene Ontology (GO)) is an extremely difficult task. Making such a digital library adaptive to any new ontology or to reorganize knowledge automatically is our main objective. The decomposition of the knowledge base into classes is a first step toward our main objective. In this paper, we will demonstrate an automated linking scheme for PubMed citations with GO terms using an improved version of extreme learning machine (ELM) type algorithms. ELM is an emergent technology, which has shown excellent performance in large data classification problems, with fast learning speeds.