Breast cancer diagnosis using genetic programming generated feature
Pattern Recognition
Classifier design with feature selection and feature extraction using layered genetic programming
Expert Systems with Applications: An International Journal
Genetically programmed-based artificial features extraction applied to fault detection
Engineering Applications of Artificial Intelligence
A generic multi-dimensional feature extraction method using multiobjective genetic programming
Evolutionary Computation
Analytical features: a knowledge-based approach to audio feature generation
EURASIP Journal on Audio, Speech, and Music Processing
Multiclass classification based on extended support vector data description
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Evolving novel image features using genetic programming-based image transforms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A support vector machine ensemble for cancer classification using gene expression data
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
A generic optimising feature extraction method using multiobjective genetic programming
Applied Soft Computing
Automatic induction of projection pursuit indices
IEEE Transactions on Neural Networks
High dimensional versus low dimensional chaos in MPEG-7 feature binding for object classification
ICISP'10 Proceedings of the 4th international conference on Image and signal processing
Expert Systems with Applications: An International Journal
Fault diagnosis on bottle filling plant using genetic-based neural network
Advances in Engineering Software
Advances in detecting parkinson's disease
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
The unconstrained automated generation of cell image features for medical diagnosis
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Two-Tier genetic programming: towards raw pixel-based image classification
Expert Systems with Applications: An International Journal
Embedding monte carlo search of features in tree-based ensemble methods
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
A Multi-Expert System for chlorine electrolyzer monitoring
Expert Systems with Applications: An International Journal
Networks of transform-based evolvable features for object recognition
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Detection of protein conformation defects from fluorescence microscopy images
Engineering Applications of Artificial Intelligence
The build of a new non-dimensional indicator for fault diagnosis in rotating machinery
International Journal of Wireless and Mobile Computing
A classifier fusion system for bearing fault diagnosis
Expert Systems with Applications: An International Journal
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One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. A GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover automatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results-using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM-have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionally, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.