Aggregating classifiers with ordinal response structure

Aggregating classifiers with ordinal response structure

Modeling Paired Ordinal Response Data

 About 25 years ago, McCullagh proposed a method for modeling univariate ordinal responses. After publishing this paper, other statisticians gradually extended his method, such that we are now able to use more complicated but efficient methods to analyze correlated multivariate ordinal data, and model the relationship between these responses and host of covariates. In this paper, we aim to...

Aggregating Ordinal Labels from Crowds by Minimax Conditional Entropy

We propose a method to aggregate noisy ordinal labels collected from a crowd of workers or annotators. Eliciting ordinal labels is important in tasks such as judging web search quality and rating products. Our method is motivated by the observation that workers usually have difficulty distinguishing between two adjacent ordinal classes whereas distinguishing between two classes which are far aw...

Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers

There is still lack of clarity about the best manner in which to handle numeric attributes when applying Bayesian network classifiers. Discretization methods entail an unavoidable loss of information. Nonetheless, a number of studies have shown that appropriate discretization can outperform straightforward use of common, but often unrealistic parametric distribution (e.g. Gaussian). Previous st...

Bayesian Learning of unobservable output aggregating multiple weak classifiers

Abstract: In this paper we propose a bayesian classification rule in a context where the training sample is not complete. The input X is observable, but the output Y is not, and we only utilize the result of weak classifiers instead of Y . Our method is motivated by epidemiological and bioinformatics problems. We prove the consistency of our method and illustrate its efficiency using simulation...