Graph-based pattern recognition techniques have been in the spotlight for many years, since there is a constant need for faster and more effective approaches. Among them, the Optimum-Path Forest (OPF) framework has gained considerable attention in the last years, mainly due to the promising results obtained by OPF-based classifiers, which range from unsupervised, semi-supervised and supervised learning. In this paper, we consider a deeper theoretical explanation concerning the supervised OPF classifier with k-neighborhood (OPF k), as well as we proposed two different training and classification algorithms that allow OPF k to work faster. The experimental validation against standard OPF and Support Vector Machines also validates the robustness of OPF k in real and synthetic datasets.

Probabilistic-driven classification techniques extend the role of traditional approaches that output labels (usually integer numbers) only. Such techniques are more fruitful when dealing with problems where one is not interested in recognition/identification only, but also into monitoring the behavior of consumers and/or machines, for instance. Therefore, by means of probability estimates, one can take decisions to work better in a number of scenarios. In this paper, we propose a probabilistic-based Optimum Path Forest (OPF) classifier to handle with binary classification problems, and we show it can be more accurate than naïve OPF in a number of datasets. In addition to being just more accurate or not, probabilistic OPF turns to be another useful tool to the scientific community.

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Machine learning techniques have been actively pursued in the last years, mainly due to the increasing number of applications that make use of some sort of intelligent mechanism for decision-making processes. In this context, we shall highlight ensemble pruning strategies, which provide heuristics to select from a collection of classifiers the ones that can really improve recognition rates and provide efficiency by reducing the ensemble size prior to combining the model. In this article, we present and validate an ensemble pruning approach for Optimum-Path Forest (OPF) classifiers based on metaheuristic optimization over general-purpose data sets to validate the effectiveness and efficiency of the proposed approach using distinct configurations in real and synthetic benchmark data sets, and thereafter, we apply the proposed approach in remote-sensing images to investigate the behaviour of the OPF classifier using pruning strategies. The image data sets were obtained from CBERS-2B, LANDSAT-5 TM, IKONOS-2 MS, and GEOEYE sensors, covering some areas of Brazil. The well-known Indian Pines data set was also used. In this work, we evaluate five different optimization algorithms for ensemble pruning, including that Particle Swarm Optimization, Harmony Search, Cuckoo Search, and Firefly Algorithm. In addition, we performed an empirical comparison between Support Vector Machine and OPF using the strategy of ensemble pruning. Experimental results showed the effectiveness and efficiency of ensemble pruning using OPF-based classification, especially concerning ensemble pruning using Harmony Search, which shows to be effective without degrading the performance when applied to large data sets, as well as a good data generalization.

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