Principal Sensitivity Analysis

  • Sotetsu Koyamada
  • Masanori Koyama
  • Ken Nakae
  • Shin Ishii
Conference paper

DOI: 10.1007/978-3-319-18038-0_48

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9077)
Cite this paper as:
Koyamada S., Koyama M., Nakae K., Ishii S. (2015) Principal Sensitivity Analysis. In: Cao T., Lim EP., Zhou ZH., Ho TB., Cheung D., Motoda H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science, vol 9077. Springer, Cham

Abstract

We present a novel algorithm (Principal Sensitivity Analysis; PSA) to analyze the knowledge of the classifier obtained from supervised machine learning techniques. In particular, we define principal sensitivity map (PSM) as the direction on the input space to which the trained classifier is most sensitive, and use analogously defined \(k\)-th PSM to define a basis for the input space. We train neural networks with artificial data and real data, and apply the algorithm to the obtained supervised classifiers. We then visualize the PSMs to demonstrate the PSA’s ability to decompose the knowledge acquired by the trained classifiers.

Keywords

Sensitivity analysis Sensitivity map PCA Dark knowledge Knowledge decomposition 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sotetsu Koyamada
    • 1
    • 2
  • Masanori Koyama
    • 1
  • Ken Nakae
    • 1
  • Shin Ishii
    • 1
    • 2
  1. 1.Graduate School of InformaticsKyoto UniversityKyotoJapan
  2. 2.ATR Cognitive Mechanisms LaboratoriesKyotoJapan

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