By Topic

Cost-sensitive Decision Tree with Missing Values and Multiple Cost Scales

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Xingyi Liu ; Radio & TV Dept., Qinzhou Univ., Qinzhou, China

Most researches focus on two costs for building cost-sensitive decision trees, such as, misclassification costs, test costs. And the existing literatures always consider the two costs as the same scales, for instance, dollars. However, in real application, it is difficult for us to regard two costs as same scales, for instance, considering misclassification cost as a dollar unit. In this paper, a new splitting attributes criterion which is combined with classification ability, test costs and misclassification costs, is proposed under the assumption of multiple-costs scales and with missing values in the dataset. The experimental results show the proposed method outperforms the existed methods in terms of the decrease of misclassification cost.

Published in:

Artificial Intelligence, 2009. JCAI '09. International Joint Conference on

Date of Conference:

25-26 April 2009