A NOVEL CLASSIFICATION METHOD OF MICROARRAY WITH RELIABILITY AND CONFIDENCE
Most of state-of-the-art machine learning algorithms cannot provide a reliable measure of their classifications and predictions. This paper addresses the importance of reliability and confidence for classification, and presents a novel method based on a combination of the unexcelled ensemble method, random forest (RF), and transductive confidence machine (TCM) which we call TCM-RF. The new algorithm hedges the predictions of RF and gives a well-calibrated region prediction by using the proximity matrix generated with RF as a nonconformity measure of examples. The new method takes advantage of RF and possesses a more precise and robust nonconformity measure. It can deal with redundant and noisy data with mixed types of variables, and is less sensitive to parameter settings. Experiments on benchmark datasets show it is more effective and robust than other TCMs. Further study on a real-world lymphoma microarray dataset shows its superiority over SVM with the ability of controlling the risk of error.
Transductive confidence machine Random forests Microarray classification
FAN YANG HUA-ZHEN WANG HONG MI
Department of Automation, Xiamen University, Xiamen, 361005, P.R.China
国际会议
2008 International Conference on Machine Learning and Cybernetics(2008机器学习与控制论国际会议)
昆明
英文
1726-1733
2008-07-12（万方平台首次上网日期，不代表论文的发表时间）