TY  - JOUR
T1  - A New Cost-Sensitive Decision Tree with Missing Values
AU - , Xingyi Liu 
JO  - Asian Journal of Information Technology
VL  - 6
IS  - 11
SP  - 1083
EP  - 1090
PY  - 2007
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2007.1083.1090
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2007.1083.1090
KW  - Cost-sensitive
KW  -decision tree
KW  -missing values
KW  -misclassification cost
AB  - 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.
ER  -