Title :
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
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;
Conference_Titel :
Artificial Intelligence, 2008. MICAI '08. Seventh Mexican International Conference on
Conference_Location :
Atizapan de Zaragoza
Print_ISBN :
978-0-7695-3441-1
DOI :
10.1109/MICAI.2008.72
