In this work, we develop a new ensemble learning framework, multi-label Random Subspace Ensemble (mRaSE), for multi-label classification. Given a base classifier (e.g., multinomial logistic reg
Ensemble clustering, which integrates multiple base clusterings to enhance robustness and accuracy, is commonly evaluated on over 10 benchmark datasets. These include 4 synthetic datasets (e.g., 3MC,a
We propose a novel “tree-averaging” model that uses the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping
