Semi-naive Bayesian classifier
EWSL-91 Proceedings of the European working session on learning on Machine learning
On the Optimality of the Simple Bayesian Classifier under Zero-One Loss
Machine Learning - Special issue on learning with probabilistic representations
Artificial Intelligence
Machine Learning
An approach to the evaluation of the performance of a discrete classifier
Pattern Recognition Letters
Making Reliable Diagnoses with Machine Learning: A Case Study
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
The foundations of cost-sensitive learning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Machine learning for medical diagnosis: history, state of the art and perspective
Artificial Intelligence in Medicine
Incorporating user control into recommender systems based on naive bayesian classification
Proceedings of the 2007 ACM conference on Recommender systems
Supervised learning based power management for multicore processors
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
| Hi-index | 0.00 |
Naive Bayes is a relatively simple classification method to, e.g., rate TV programs as interesting or uninteresting to a user. In case the training set consists of instances, chosen randomly from the instance space, the posterior probability estimates are random variables. Their statistical properties can be used to calculate confidence intervals around them, enabling more refined classification strategies than the usual argmax-operator. This may alleviate the cold-start problem and provide additional feedback to the user. In this paper, we give an explicit expression to estimate the variances of the posterior probability estimates from the training data and investigate the strategy that refrains from classification in case the confidence interval around the largest posterior probability overlaps with any of the other intervals. We show that the classification error rate can be significantly reduced at the cost of a lower coverage, i.e., the fraction of classifiable instances, in a TV-program recommender.