Asian Journal of Information Technology
ISSN: Online 1993-5994ISSN: Print 1682-3915
Abstract
Cost-sensitive learning is popular during the process of classification. Most researches focus on two costs for building cost-sensitive decision trees, such as, misclassification costs, test costs. In this study, a novel splitting attributes criterion is proposed firstly. And a test strategy combining discount costs for decreasing the misclassification cost is presented with missing values in test set after the cost-sensitive decision tree are constructed with missing values in training sets. Finally, the experimental results show our method outperform the existed methods in terms of the decrease of misclassification cost.
How to cite this article:
Xingyi Liu . A New Cost-Sensitive Decision Tree with Missing Values.
DOI: https://doi.org/10.36478/ajit.2007.1083.1090
URL: https://www.makhillpublications.co/view-article/1682-3915/ajit.2007.1083.1090