Improving optimum-path forest learning using bag-of-classifiers and 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 ensemble of optimum-path forest (OPF) classifiers, which consists into combining different instances that compute a score-based confidence level for each training sample in order to turn the classification process “smarter”, i.e., more reliable. Such confidence level encodes the level of effectiveness of each training sample, and it can be used to avoid ties during the OPF competition process. Experimental results over fifteen benchmarking datasets have shown the effectiveness and efficiency of the proposed approach for classification problems, with more accurate results in more than 67% of the datasets considered in this work. Additionally, we also considered a bagging strategy for comparison purposes, and we showed the proposed approach can lead to considerably better results.