Genetic Programming and Evolvable Machines
A Function-Based Classifier Learning Scheme Using Genetic Programming
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Intelligent GP fusion from multiple sources for text classification
Proceedings of the 14th ACM international conference on Information and knowledge management
Enhancing Knowledge Discovery via Association-Based Evolution of Neural Logic Networks
IEEE Transactions on Knowledge and Data Engineering
A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining
Computational Optimization and Applications
Genetic parallel programming: design and implementation
Evolutionary Computation
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
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
Design of a two-stage fuzzy classification model
Expert Systems with Applications: An International Journal
Connection Science - Evolutionary Learning and Optimisation
A GP Based Approach to the Classification of Multiclass Microarray Datasets
ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Artificial Intelligence
Rule extraction for classification of acoustic emission signals using Ant Colony Optimisation
Engineering Applications of Artificial Intelligence
Memetic programming with adaptive local search using tree data structures
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
A Genetic Programming Classifier Design Approach for Cell Images
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Particle swarm optimized multiple regression linear model for data classification
Applied Soft Computing
Induction machine fault detection using clone selection programming
Expert Systems with Applications: An International Journal
A hybrid evolutionary algorithm for attribute selection in data mining
Expert Systems with Applications: An International Journal
Kernel Trees for Support Vector Machines
IEICE - Transactions on Information and Systems
Qualitative classification of descent phases in commercial flight data
International Journal of Computational Intelligence Studies
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
Parallel programs are more evolvable than sequential programs
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
EuroGP'03 Proceedings of the 6th European conference on Genetic programming
A survey on the application of genetic programming to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
G3P-MI: A genetic programming algorithm for multiple instance learning
Information Sciences: an International Journal
Learning to detect web spam by genetic programming
WAIM'10 Proceedings of the 11th international conference on Web-age information management
CLONAL-GP framework for artificial immune system inspired genetic programming for classification
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Semi-supervised genetic programming for classification
Proceedings of the 13th annual conference on Genetic and evolutionary computation
DepthLimited crossover in GP for classifier evolution
Computers in Human Behavior
Two layered Genetic Programming for mixed-attribute data classification
Applied Soft Computing
Evolutionary generation of prototypes for a learning vector quantization classifier
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
A novel genetic programming based approach for classification problems
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
HAIS'10 Proceedings of the 5th international conference on Hybrid Artificial Intelligence Systems - Volume Part I
Lazy learning for multi-class classification using genetic programming
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing Theories and Applications: with aspects of artificial intelligence
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Artificial Intelligence in Medicine
Human action recognition from multi-sensor stream data by genetic programming
EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation
A new evaluation measure for color image segmentation based on genetic programming approach
Image and Vision Computing
Hi-index | 0.01 |
Explores the feasibility of applying genetic programming (GP) to multicategory pattern classification problem. GP can discover relationships and express them mathematically. GP-based techniques have an advantage over statistical methods because they are distribution-free, i.e., no prior knowledge is needed about the statistical distribution of the data. GP also automatically discovers the discriminant features for a class. GP has been applied for two-category classification. A methodology for GP-based n-class classification is developed. The problem is modeled as n two-class problems, and a genetic programming classifier expression (GPCE) is evolved as a discriminant function for each class. The GPCE is trained to recognize samples belonging to its own class and reject others. A strength of association (SA) measure is computed for each GPCE to indicate the degree to which it can recognize samples of its own class. SA is used for uniquely assigning a class to an input feature vector. Heuristic rules are used to prevent a GPCE with a higher SA from swamping one with a lower SA. Experimental results are presented to demonstrate the applicability of GP for multicategory classification, and they are found to be satisfactory. We also discuss the various issues that arise in our approach to GP-based classification, such as the creation of training sets, the role of incremental learning, and the choice of function set in the evolution of GPCE, as well as conflict resolution for uniquely assigning a class