Title :
TCM-RF : Hedging the predictions of random forest
Author :
Wang, Huazhen ; Yang, Fan ; Wu, Wujie ; Lin, Chengde
Author_Institution :
Dept. of Autom., Xiamen Univ., Xiamen
Abstract :
The output of traditional classifier is point prediction without giving any confidence of it. To the contrary, transductive confidence machine (TCM), which is a novel framework that provides a prediction result coupled with its accurate confidence. This method also can hedge the prediction in which the predicting accuracy will be controlled by predefined confidence level. In the framework of TCM, the efficiency of prediction depends on the strangeness function of samples. This paper incorporates random forests (RF) into the framework of TCM and proposes new TCM algorithm named TCM-RF, in which the strangeness obtained by RF will be used to implement the confidence prediction. Compared with traditional TCM algorithms, our method benefits from the more precise and robust strangeness measure and takes advantage of random forest. Experiments indicate its effectiveness and robustness. In addition, our study demonstrated that using ensemble strategies to define sample strangeness may be a more principled way than using a single classifier. On the other hand, it also shows that the paradigm of hedging prediction can be applied to an ensemble classifier.
Keywords :
forestry; inference mechanisms; learning (artificial intelligence); TCM-RF; ensemble classifier; random forest; robust strangeness measure; transductive confidence machine; Accuracy; Automation; Calibration; Error correction; Intelligent control; Radio frequency; Robustness; Support vector machines; Testing; Turing machines; confidence machine; hedging prediction; random forests; transductive confidence machine;
Conference_Titel :
Intelligent Control and Automation, 2008. WCICA 2008. 7th World Congress on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4244-2113-8
Electronic_ISBN :
978-1-4244-2114-5
DOI :
10.1109/WCICA.2008.4593029