Incremental Local Linear Fuzzy Classifier in Fisher Space

Abstract

Optimizing the antecedent part of neurofuzzy system is an active research topic, for which different approaches have been developed. However, 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 neurofuzzy 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 neurofuzzy classifier is then built in the transformed space, starting from the simplest form (a global linear classifier). If the overall performance of the classifier was not satisfactory, it would be iteratively refined by incorporating additional local classifiers. In addition, rule consequent parameters are optimized using a local least square approach. Our refinement strategy is motivated by LOLIMOT, which is a greedy partition algorithm for structure training and has been successfully applied in a number of identification problems. The proposed classifier is compared to several benchmark classifiers on a number of well-known datasets. The results prove the efficacy of the proposed classifier in achieving high performance while incurring low computational effort.

Mt-CGP

“…Linear Bayes [6] 73.0 Quadratic Bayes [6] 75.4 Fuzzy Classifier [6] 76.9 Quadratic Bayes [10] 78.7 Neural network [6] 79.2 Piecewise linear [6] 82.2 C4.5 [6] 83.9 MLP Neural Network [10] 86.3 k-Nearest Neighbor [10] 87.8…”

An approach to performance assessment and fault diagnosis for rotating machinery equipment

“…Cluster analysis can classify samples into corresponding groups based on the measured parameters, and hierarchical cluster analysis (HCA) is the most commonly used clustering tool [13,14]. FDA provides an optimal lower dimensional representation, in terms of maximizing the separability among different populations representative of different operational states, by projecting normal and fault populations, and separating them to the limit in the reconstructed space [15][16][17]. In the reconstructed low-dimensional space, the MD between the new measurement data and the normal population, constructed using normal data, can be calculated for performance assessment.…”