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Anti-germ Performance Prediction for Detergents Based on Elman Network on Small Data Sets

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Advanced Data Mining and Applications (ADMA 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5678))

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Abstract

Anti-germ performance test is critical in the production of detergents. However, actual biochemical tests are often costly and time consuming. In this paper, we present a neural network based model to predict the performance. The model made it much faster and cost less than doing actual biochemical tests. We also present preprocessing methods that can reduce data conflicts while keeping the monotonicity on small data sets. This model performs well though the training data sets are small. Its input is the actual value of key ingredients, which is not widely used in solving biochemical problems. The results of experiments are generated on the base of two detergent products for two types of bacteria, and appear reasonable according to natural rules. The prediction results show a high precision and fitting with the monotonicity rule mostly. Experts in biochemical area also give good evaluations to the proposed model.

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Authors and Affiliations

  1. State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, 100084, Beijing

    Anqi Cui, Hua Xu & Peifa Jia

Authors
  1. Anqi Cui
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  2. Hua Xu
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  3. Peifa Jia
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Editor information

Editors and Affiliations

  1. Knowledge Science & Engineering Institute, School of Education Technology, Beijing Normal University, Xinjiekouwai Ave. 19, 100875, Beijing, China

    Ronghuai Huang

  2. The Hong Kong University of Science and Technology, Clear Water Bay,, Hong Kong, Hong Kong

    Qiang Yang

  3. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  4. Faculty of Economics, University of Porto, Rua Dr. Roberto Frias, 4200-465, Porto, Portugal

    João Gama

  5. School of Information, Zhongguancum, Renmin University, 100872, Beijing, China

    Xiaofeng Meng

  6. School of Information Technology and Electrical Engineering, The University of Queensland, 4072, St. Lucia, Queensland, Australia

    Xue Li

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© 2009 Springer-Verlag Berlin Heidelberg

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Cui, A., Xu, H., Jia, P. (2009). Anti-germ Performance Prediction for Detergents Based on Elman Network on Small Data Sets. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2009. Lecture Notes in Computer Science(), vol 5678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03348-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-03348-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03347-6

  • Online ISBN: 978-3-642-03348-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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