Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-class ROC analysis from a multi-objective optimisation perspective
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
Memetic NSGA A Multi-Objective Genetic Algorithm for Classification of Microarray Data
ADCOM '07 Proceedings of the 15th International Conference on Advanced Computing and Communications
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Information Processing Letters
Genetic algorithms for gene expression analysis
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
An experimental test of Occam's razor in classification
Machine Learning
Hi-index | 0.00 |
Microarray data allows an unprecedented view of the biochemical mechanisms contained within a cell although deriving useful information from the data is still proving to be a difficult task. In this paper, a novel method based on a multi-objective genetic algorithm is investigated that evolves a near-optimal trade-off between Artificial Neural Network ANN classifier accuracy sensitivity and specificity and size number of genes. This hybrid method is shown to work on four well-established gene expression data sets taken from the literature. The results provide evidence for the rule discovery ability of the hybrid method and indicate that the approach can return biologically intelligible as well as plausible results and requires no pre-filtering or pre-selection of genes.