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Improved Kernel Density Estimation Self-organizing Incremental Neural Network to Perform Big Data Analysis

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11302))

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Abstract

Plenty of data are generated continuously due to the progress in the field of network technology. Additionally, some data contain substantial noise, while other data vary their properties in according to various real time scenarios. Owing to these factors, analyzing big data is difficult. To address these problems, an adaptive kernel density estimation self-organizing neural network (AKDESOINN) has been proposed. This approach is based on the kernel density estimation self-organizing incremental neural network (KDESOINN), which is an extension of the self-organizing incremental neural network (SOINN). An SOINN can study the distribution using the input data online, while KDESOINN can estimate the probability density function based on this information. The AKDESOINN can adapt itself to the changing data properties by estimating the probability density function. Further, the experimental results depict that AKDESOINN succeeds in maintaining the performance of KDESOINN, while depicting an ability to adapt to the changing data.

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Author information

Authors and Affiliations

  1. Department of Systems and Control Engineering, Tokyo Institute of Technology, Tokyo, Japan

    Wonjik Kim & Osamu Hasegawa

  2. Inc.SOINN, Cureindo-Building 405, Turuma8-4-30, Tamachi, Tokyo, Japan

    Osamu Hasegawa

Authors
  1. Wonjik Kim
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  2. Osamu Hasegawa
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Corresponding author

Correspondence to Wonjik Kim .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Kim, W., Hasegawa, O. (2018). Improved Kernel Density Estimation Self-organizing Incremental Neural Network to Perform Big Data Analysis. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_1

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  • DOI: https://doi.org/10.1007/978-3-030-04179-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04178-6

  • Online ISBN: 978-3-030-04179-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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