Performance of Inductive Method of Model Self-Organization with Incomplete Model and Noisy Data

Author

Ponomareva, Natalia ; Alexandrov, Mikhail ; Gelbukh, Alexander

Author_Institution

Univ. of Wolverhampton, Wolverhampton

fYear

2008

fDate

27-31 Oct. 2008

Firstpage

101

Lastpage

108

Abstract

Inductive method of model self-organization (IMMSO) developed in 80s by A. Ivakhnenko is an evolutionary machine learning algorithm, which allows selecting a model of optimal complexity that describes or explains a limited number of observation data when any a priori information is absent or is highly insufficient. In this paper, we study the performance of IMMSO to reveal a model in a given class with different volumes of data, contributions of unaccounted components, and levels of noise. As a simple case study, we consider artificial observation data: the sum of a quadratic parabola and cosine; model class under consideration is a polynomial series. The results are interpreted in the terms of signal-noise ratio.

Keywords

data mining; learning (artificial intelligence); artificial observation data; evolutionary machine learning algorithm; inductive method performance; model self-organization; noisy data; polynomial series; quadratic parabola; signal-noise ratio; Artificial intelligence; Computer networks; Data mining; Europe; High performance computing; Machine learning algorithms; Noise level; Polynomials; Social network services; Training data; Data Mining; Inductive Modeling; Machine Learning; Noise Sensibility; Precision;

fLanguage

English

Publisher

ieee

Conference_Titel

Artificial Intelligence, 2008. MICAI '08. Seventh Mexican International Conference on

Conference_Location

Atizapan de Zaragoza

Print_ISBN

978-0-7695-3441-1

Type

conf

DOI

10.1109/MICAI.2008.72

Filename

4682450