Principal Sensitivity Analysis

Sotetsu Koyamada; Masanori Koyama; Ken Nakae; Shin Ishii

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

Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2015: Advances in Knowledge Discovery and Data Mining pp 621-632

Principal Sensitivity Analysis

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Authors and affiliations

Sotetsu KoyamadaEmail author
Masanori Koyama
Ken Nakae
Shin Ishii

Conference paper

First Online:: 17 April 2015

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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Authors and Affiliations

Sotetsu Koyamada
- 1
- 2
Email author
Masanori Koyama
- 1
Ken Nakae
- 1
Shin Ishii
- 1
- 2

1.Graduate School of InformaticsKyoto UniversityKyotoJapan
2.ATR Cognitive Mechanisms LaboratoriesKyotoJapan

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Principal Sensitivity Analysis

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