Network Optimization based on Genetic Algorithm for High-Level Data Classification



Complex Networks, Genetic Algorithms, Network Optimization, Graph Optimization, Particle swarm optimization, High Data Classification


High-level data classification techniques are capable of considering not only physical aspects of the data, such as space, distance, proximity, distribution, but can also consider their functional, topological and structural aspects. High-level techniques are commonly defined in two major steps: the construction of a network from the feature vector data and the uncovering of its underlying patterns using complex networks properties. In the network construction step, heuristics based on k-nearest neighbors strategies have been widely adopted, while several complex network measures (e.g. PageRank) have been modeled to learn high-level patterns of the input data. As both steps are directly related, i.e., the network configuration impacts directly the results obtained by the classifier, in this paper we develop a genetic algorithm (GA) to optimize the network construction step. To be specific, we hypothesize that the salient features of GAs, such as their robust search mechanism and binary representation, may provide a more powerful network representation in the context of the high-level classification based on importance characterization. In summary, extensive experiments with real data sets demonstrate that the networks provided by our GA strategy achieved higher predictive accuracy than those of a widely adopted method based on the nearest neighbors heuristic and competitive results against state-of-the-art ones.


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Author Biographies

Janayna Moura Fernandes, Universidade Federal de Uberlândia

Janayna Moura Fernandes received the B.S. degree in Information Systems from the Federal University of Uberlandia, Brazil in 2019. She is currently a M.Sc. candidate in Computer Science at Federal University of Uberlandia and has interests in the topics of nature-inspired optimization and complex systems.

Gina Maira Barbosa de Oliveira, Federal University of Uberlandia

Gina Maira Barbosa de Oliveira received the B.S. degree from the Federal University of Uberlandia, Brazil, in 1990, and the M.Sc. and Ph.D. from the Aeronautics Institute of Technology, Brazil, respectively in 1992 and 1999. She is a Professor with the Faculty of Computing, Federal University of Uberlandia, Brazil and has experience on the following topics: genetic algorithms, cellular automata, evolutionary computing and artificial intelligence.

Murillo Guimarães Carneiro, Federal University of Uberlandia

Murillo Guimaraes Carneiro (M'17-SM'20) received the PhD degree from the University of Sao Paulo, Brazil in 2016; the M.Sc. degree from the Federal University of Uberlandia, Brazil in 2012; and the Tech. degree from the Goiano Federal Institute, Brazil in 2008. He is an Assistant Professor with the Faculty of Computing, Federal University of Uberlandia, Brazil. His research interests include machine learning, complex networks, network-based learning and nature-inspired computing. Dr. Carneiro was a recipient of the Google Latin America Research Awards in 2020 and 2022.


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How to Cite

Moura Fernandes, J., Barbosa de Oliveira, G. M., & Guimarães Carneiro, M. (2022). Network Optimization based on Genetic Algorithm for High-Level Data Classification. IEEE Latin America Transactions, 21(2), 295–301. Retrieved from