Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two Variations on Fisher's Linear Discriminant for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Linear Dimensionality Reduction via a Heteroscedastic Extension of LDA: The Chernoff Criterion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
Application of genetic programming for multicategory patternclassification
IEEE Transactions on Evolutionary Computation
A comparison of linear genetic programming and neural networks inmedical data mining
IEEE Transactions on Evolutionary Computation
Feature generation using genetic programming with application to fault classification
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Pairwise Rayleigh quotient classifier with application to the analysis of breast tumors
SPPR'07 Proceedings of the Fourth conference on IASTED International Conference: Signal Processing, Pattern Recognition, and Applications
Representation and dimensionality reduction of semantically enriched clickstreams
Ph.D. '08 Proceedings of the 2008 EDBT Ph.D. workshop
Texture segmentation by genetic programming
Evolutionary Computation
Dynamic population variation in genetic programming
Information Sciences: an International Journal
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
A survey and taxonomy of performance improvement of canonical genetic programming
Knowledge and Information Systems
Pairwise Rayleigh quotient classifier with application to the analysis of breast tumors
SPPRA '07 Proceedings of the Fourth IASTED International Conference on Signal Processing, Pattern Recognition, and Applications
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Artificial Intelligence in Medicine
Multi-objective genetic programming for visual analytics
EuroGP'11 Proceedings of the 14th European conference on Genetic programming
Breast cancer detection using cartesian genetic programming evolved artificial neural networks
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
Information Sciences: an International Journal
EvoBIO'13 Proceedings of the 11th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Evolving artificial neural networks for nonlinear feature construction
Proceedings of the 15th annual conference on Genetic and evolutionary computation
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This paper proposes a novel method for breast cancer diagnosis using the feature generated by genetic programming (GP). We developed a new feature extraction measure (modified Fisher linear discriminant analysis (MFLDA)) to overcome the limitation of Fisher criterion. GP as an evolutionary mechanism provides a training structure to generate features. A modified Fisher criterion is developed to help GP optimize features that allow pattern vectors belonging to different categories to distribute compactly and disjoint regions. First, the MFLDA is experimentally compared with some classical feature extraction methods (principal component analysis, Fisher linear discriminant analysis, alternative Fisher linear discriminant analysis). Second, the feature generated by GP based on the modified Fisher criterion is compared with the features generated by GP using Fisher criterion and an alternative Fisher criterion in terms of the classification performance. The classification is carried out by a simple classifier (minimum distance classifier). Finally, the same feature generated by GP is compared with a original feature set as the inputs to multi-layer perceptrons and support vector machine. Results demonstrate the capability of this method to transform information from high-dimensional feature space into one-dimensional space and automatically discover the relationship among data, to improve classification accuracy.