TY - GEN
T1 - Incremental locally linear fuzzy classifier
AU - Eftekhari, Armin
AU - Khanesar, Mojtaba Ahmadieh
AU - Forouzanfar, Mohamad
AU - Teshnehlab, Mohammad
PY - 2009
Y1 - 2009
N2 - Optimizing the antecedent part of neuro-fuzzy system is investigated in a number of documents. Current approaches typically suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neuro-fuzzy systems that are structured based on local linear classifiers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neuro-fuzzy classifier is built in the transformed space, starting from the simplest form. In addition, rule consequent parameters are optimized using a local least square approach.
AB - Optimizing the antecedent part of neuro-fuzzy system is investigated in a number of documents. Current approaches typically suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neuro-fuzzy systems that are structured based on local linear classifiers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neuro-fuzzy classifier is built in the transformed space, starting from the simplest form. In addition, rule consequent parameters are optimized using a local least square approach.
UR - https://www.scopus.com/pages/publications/84903598359
U2 - 10.1007/978-3-540-89619-7_30
DO - 10.1007/978-3-540-89619-7_30
M3 - Article in proceedings
AN - SCOPUS:84903598359
SN - 9783540896180
VL - 58
T3 - Advances in Intelligent and Soft Computing
SP - 305
EP - 314
BT - Applications of Soft Computing
PB - Springer Verlag
ER -