Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains

Washio, Takashi; Motoda, Hiroshi; Niwa, Yuji

Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains

Takashi Washio, Hiroshi Motoda, Yuji Niwa

Download BibTeX Cite

Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence. Machine Learning. Pages 772-778.

Abstract

Most conventional law equation discovery systems such as BACON require experimental environments to acquire their necessary data. The mathematical techniques such as linear system identification and neural network fitting presume the classes of equations to model given observed data sets. The study reported in this paper proposes a novel method to discover an admissible model equation from a given set of observed data, while the equation is ensured to reflect first principles governing the objective system . The power of the proposed method comes from the use of the scale-types of the observed quantities, a mathematical property of identity and quasi-bi-variate fitting to the given data set. Its principles and algorithm are described with moderately complex examples, and its practicality is demonstrated through a real application to psychological and sociological law equation discovery.

Takashi Washio, Hiroshi Motoda, and Yuji Niwa. 1999. Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains. In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99. IJCAI, 772–778. https://ijcai.org/proceedings/1999/110

@inproceedings{ijcai1999p110,
  title     = {Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains},
  author    = {Washio, Takashi and Motoda, Hiroshi and Niwa, Yuji},
  booktitle = {Proceedings of the Sixteenth International Joint Conference on
               Artificial Intelligence, {IJCAI-99}},
  publisher = {International Joint Conferences on Artificial Intelligence},
  editor    = {Thomas Dean},
  pages     = {772--778},
  year      = {1999},
  note      = {Machine Learning},
  url       = {https://ijcai.org/proceedings/1999/110},
}

TY  - CPAPER
TI  - Discovering Admissible Model Equations from Observed Data Based on Scale-Types and Identity Constrains
AU  - Washio, Takashi
AU  - Motoda, Hiroshi
AU  - Niwa, Yuji
T2  - Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI-99
PB  - International Joint Conferences on Artificial Intelligence
PY  - 1999
SP  - 772
EP  - 778
UR  - https://ijcai.org/proceedings/1999/110
ER  -