Fault classification using genetic programming

@article{Zhang2007, abstract = {Genetic programming (GP) is a stochastic process for automatically generating computer programs. In this paper, three GP-based approaches for solving multi-class classification problems in roller bearing fault detection are proposed. Single-GP maps all the classes onto the one-dimensional GP output. Independent-GPs singles out each class separately by evolving a binary GP for each class independently. Bundled-GPs also has one binary GP for each class, but these GPs are evolved together with the aim of selecting as few features as possible. The classification results and the features each algorithm has selected are compared with genetic algorithm (GA) based approaches GA/ANN and GA/SVM. Experiments show that bundled-GPs is strong in feature selection while retaining high performance, which equals or outperforms the two previous GA-based approaches.}, author = {Zhang, Liang and Nandi, Asoke K. }, citeulike-article-id = {2789279}, doi = {http://dx.doi.org/10.1016/j.ymssp.2006.04.004}, journal = {Mechanical Systems and Signal Processing}, month = {April}, number = {3}, pages = {1273--1284}, posted-at = {2008-05-12 12:41:46}, priority = {2}, title = {Fault classification using genetic programming}, url = {http://dx.doi.org/10.1016/j.ymssp.2006.04.004}, volume = {21}, year = {2007} }

TY - JOUR ID - Zhang2007 L3 - citeulike-article-id:2789279 N2 - Genetic programming (GP) is a stochastic process for automatically generating computer programs. In this paper, three GP-based approaches for solving multi-class classification problems in roller bearing fault detection are proposed. Single-GP maps all the classes onto the one-dimensional GP output. Independent-GPs singles out each class separately by evolving a binary GP for each class independently. Bundled-GPs also has one binary GP for each class, but these GPs are evolved together with the aim of selecting as few features as possible. The classification results and the features each algorithm has selected are compared with genetic algorithm (GA) based approaches GA/ANN and GA/SVM. Experiments show that bundled-GPs is strong in feature selection while retaining high performance, which equals or outperforms the two previous GA-based approaches. IS - 3 JF - Mechanical Systems and Signal Processing EP - 1284 TI - Fault classification using genetic programming VL - 21 SP - 1273 AU - Zhang, Liang AU - Nandi, Asoke PY - 2007/04// UR - http://dx.doi.org/10.1016/j.ymssp.2006.04.004 ER -