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Authors: Yasuhiro Kirihata ; Takuya Maekawa and Takashi Onoyama

Affiliation: Hitachi Solutions, Ltd., 4-12-7 Higashishinagawa, Shinagawa-ku, Tokyo, 140-0002, Japan

ISBN: 978-989-758-350-6

Keyword(s): Machine Learning, Causality Analysis, Nonlinear Classification Model, Self-Organizing Map, Local Linear Model.

Abstract: In terms of nonlinear machine learning classifier such as Deep Learning, machine-learning model is generally a black box which has issue not to be clear the causality among its output classification and input attributes. In this paper, we propose a causality analysis method with self-organizing map and locally approximation to linear model. In this method, self-organizing map generates the cluster of input data and local linear models for each node on the map provides explanation of the generated model. Applying this method to the member rank prediction model based on Deep Learning, we validated our proposed method.

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Paper citation in several formats:
Kirihata, Y.; Maekawa, T. and Onoyama, T. (2019). A Causality Analysis for Nonlinear Classification Model with Self-Organizing Map and Locally Approximation to Linear Model.In Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-350-6, pages 419-426. DOI: 10.5220/0007258404190426

@conference{icaart19,
author={Yasuhiro Kirihata. and Takuya Maekawa. and Takashi Onoyama.},
title={A Causality Analysis for Nonlinear Classification Model with Self-Organizing Map and Locally Approximation to Linear Model},
booktitle={Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2019},
pages={419-426},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007258404190426},
isbn={978-989-758-350-6},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - A Causality Analysis for Nonlinear Classification Model with Self-Organizing Map and Locally Approximation to Linear Model
SN - 978-989-758-350-6
AU - Kirihata, Y.
AU - Maekawa, T.
AU - Onoyama, T.
PY - 2019
SP - 419
EP - 426
DO - 10.5220/0007258404190426

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