Improving Optimum-Path Forest Classification Using Confidence Measures

Machine learning techniques have been actively pursued in the last years, mainly due to the great number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this work, we presented an improved version of the Optimum-Path Forest classifier, which learns a score-based confidence level for each training sample in order to turn the classification process "smarter", i.e., more reliable. Experimental results over 20 benchmarking datasets have showed the effectiveness and efficiency of the proposed approach for classification problems, which can obtain more accurate results, even on smaller training sets.