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.