Multiview data analysis has become increasingly important for integrating heterogeneous information from multiple data sources. Cooperative learning provides a flexible framework for multiview integration by bridging early and late fusion strategies. However, its use of squared loss makes it sensitive to outliers. In t...
Interpretable prediction models are important in biomedical research, where predictive accuracy must often be balanced against the ability to examine predictor-outcome relationships. Automatic Binary Logistic Estimation (ABLE) provides interpretable rule-based representations by constructing additive models using thres...
Yu-Long Li, Ke Wan, Toshio Shimokawa et al.· 0 citations
This work proposes Cooperative Learning with a penalized Linear Mixed Model (CL-pLMM) for high-dimensional multiview data with a clustered structure and shows that its objective function can be represented as a penalized linear mixed-effects model applied to augmented data, allowing existing estimation procedures to be...
Accurate diagnostic and risk-prediction models are important for supporting clinical decision-making during infectious disease outbreaks. However, privacy and governance requirements may restrict patient-level data sharing across healthcare institutions, and data distributions often vary. Moreover, AUC is widely used t...
Mayu Hiraishi, Kensuke Tanioka, Toshio Shimokawa· 0 citations
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