Semi-naive Bayesian classifiers seek to retain the numerous strengths of naive Bayes while reducing error by weakening the attribute independence assumption. Backwards Sequential ...
In practical classification, there is often a mix of learnable and unlearnable classes and only a classifier above a minimum performance threshold can be deployed. This problem is...
Abstract. Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there ma...
We propose a new decision tree algorithm, Class Confidence Proportion Decision Tree (CCPDT), which is robust and insensitive to class distribution and generates rules which are st...
Wei Liu, Sanjay Chawla, David A. Cieslak, Nitesh V...
In an idealized gated radiotherapy treatment, radiation is delivered only when the tumor is at the right position. For gated lung cancer radiotherapy, it is difficult to generate ...
Ying Cui, Jennifer G. Dy, Gregory C. Sharp, Brian ...