Cost-sensitive Performance Metric for Comparing Multiple Ordinal Classifiers.

ABSTRACT: The surge of interest in personalized and precision medicine during recent years has increased the application of ordinal classification problems in biomedical science. Currently, accuracy, Kendall's τ_b , and average mean absolute error are three commonly used metrics for evaluating the effectiveness of an ordinal classifier. Although there are benefits to each, no single metric considers the benefits of predictive accuracy with the tradeoffs of misclassification cost. In addition, decision analysis that considers pairwise analysis of the metrics is not trivial due to inconsistent findings. A new cost-sensitive metric is proposed to find the optimal tradeoff between the two most critical performance measures of a classification task - accuracy and cost. The propose

SUBMITTER: George NI 

PROVIDER: S-EPMC5217743 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature