The performance of m-out-of-n bagging with and without replacement in terms of the sampling ratio (m/n) is analyzed. Standard bagging uses resampling with replacement to generate ...
Abstract. It is well known that diversity among component classifiers is crucial for constructing a strong ensemble. Most existing ensemble methods achieve this goal through resam...
An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between a...
The “chicken-egg” dilemma between VLSI interconnect timing optimization and delay calculation suggests an iterative approach. We separate interconnect timing transformation as...
We introduce a new ensemble method based on decision tree to discover significant and diversified rules for subtype classification of childhood acute lymphoblastic leukemia, a ...