We study cost-sensitive learning of decision trees that incorporate both test costs and misclassification costs. In particular, we first propose a lazy decision tree learning that ...
In medical diagnosis doctors must often determine what medical tests (e.g., X-ray, blood tests) should be ordered for a patient to minimize the total cost of medical tests and mis...
Shengli Sheng, Charles X. Ling, Ailing Ni, Shichao...
Cost-sensitive decision tree and cost-sensitive naïve Bayes are both new cost-sensitive learning models proposed recently to minimize the total cost of test and misclassifications...
— A number of advanced sampling strategies have been proposed in recent years to address the narrow passage problem for probabilistic roadmap (PRM) planning. These sampling strat...
Various approaches for dealing with missing data have been developed so far. In this paper, two strategies are proposed for cost-sensitive iterative imputing missing values with o...