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FLSOM with Different Rates for Classification in Imbalanced Datasets

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Artificial Neural Networks - ICANN 2008 (ICANN 2008)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5163))

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Abstract

There are several successful approaches dealing with imbalanced datasets. In this paper, the Fuzzy Labeled Self-Organizing Map (FLSOM) is extended to work with that type of data. The proposed approach is based on assigning two different values in the learning rate depending on the data vector membership of the class. The technique is tested with several datasets and compared with other approaches. The results seem to prove that FLSOM with different rates is a suitable tool and allows understanding and visualizing the data such as overlapped clusters.

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Authors and Affiliations

  1. Electrónica de Computadores y Sistemas. Edificio Departamental, Universidad de Oviedo. Escuela Politécnica Superior de Ingeniería. Departamento de Ingeniería Eléctrica, 2. Zona Oeste. Campus de Viesques s/n., 33204, Gijón/Xixón (Asturies), Spain

    Iván Machón-González & Hilario López-García

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  1. Iván Machón-González
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  2. Hilario López-García
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Véra Kůrková Roman Neruda Jan Koutník

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Machón-González, I., López-García, H. (2008). FLSOM with Different Rates for Classification in Imbalanced Datasets. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_66

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  • DOI: https://doi.org/10.1007/978-3-540-87536-9_66

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-87535-2

  • Online ISBN: 978-3-540-87536-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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