We experimentally evaluate bagging and six other randomization-based approaches to creating an ensemble of decision-tree classifiers. Bagging uses randomization to create multipl...
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bow...
Background: Automated, image based high-content screening is a fundamental tool for discovery in biological science. Modern robotic fluorescence microscopes are able to capture th...
Ensemble learning is attracting much attention from pattern recognition and machine learning domains for good generalization. Both theoretical and experimental researches show tha...
Random subspaces are a popular ensemble construction technique that improves the accuracy of weak classifiers. It has been shown, in different domains, that random subspaces combi...
Abstract—We experimentally evaluate bagging and seven other randomizationbased approaches to creating an ensemble of decision tree classifiers. Statistical tests were performed o...
Robert E. Banfield, Lawrence O. Hall, Kevin W. Bow...