Ensemble algorithms can improve the performance of a given learning algorithm through the combination of multiple base classifiers into an ensemble. In this paper, the idea of usin...
We present a new ensemble learning method that employs a set of regional classifiers, each of which learns to handle a subset of the training data. We split the training data and ...
We empirically evaluate several state-of-theart methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to se...
Since accurate classification of DNA microarray is a very important issue for the treatment of cancer, it is more desirable to make a decision by combining the results of various ...
In this paper, a new method is proposed to detect abnormal regions in colonoscopic images by patch-based classifier ensemble. Through supervised learning from image patches of var...
Kap Luk Chan, Peng Li, Shankar Muthu Krishnan, Yan...