Multi-label Classification using Logistic Regression Models for NTCIR-7 Patent Mining Task
Akinori Fujino, Hideki Isozaki · 2008
We design a multi-label classification system based on a machine learning approach for the NTCIR-7 Patent Mining Task. In our system, we employ a logistic regression model for each International Patent Classification (IPC) code that determines the IPC code assignment of research papers. The logistic regression models are trained by using patent documents provided by task organizers. To mitigate the overfitting of the logistic regression models to the patent documents, we design the feature vectors of the patent documents with feature weighting and component selection methods utilizing a research paper set. Using a test collection for the Japanese subtask of the NTCIR-7 Patent Mining Task, we confirmed the effectiveness of our multi-label classification system.