An incremental Optimum-Path Forest classifier and its application to non-technical losses identification. (October 2021) - Iwashita, Adriana Sayuri; Rodrigues, Douglas; Gastaldello, Danilo Sinkiti; de Souza, Andre Nunes; Papa, João Paulo
Abstract: Non-technical losses stand for the energy consumed but not billed, affecting the energy grid as a whole. Such an issue somehow prevails in developing countries, harming the quality of energy and preventing social programs benefit from tax revenues. Machine learning techniques can help mitigate it by mining information from fraudsters and legal users for further decision-making. In this paper, we deal with a steady increase of dataset size, i.e., the incremental learning problem, which can cope with datasets regularly provided by energy companies, requiring the learner to be updated constantly. Since repeating the entire learning process might be prohibitive, adjusting the model to the new data shows to be a better choice. We propose an incremental Optimum-Path Forest approach with k -nn neighborhood that is considerably more efficient for training than its counterpart version, with experiments validated in general-purpose datasets and also in the context of non-technical losses identification. Graphical abstract: Highlights: A new incremental learning algorithm for the Optimum-Path Forest classifier. Proposed approach outperformed its counterpart in terms of efficiency. First OPF-based incremental learning applied to non-technical losses identification.