A note on genetic algorithms for large-scale feature selection
Handbook of pattern recognition & computer vision
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IEEE Transactions on Knowledge and Data Engineering
Machine Learning
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Proceedings of the IIS'2000 Symposium on Intelligent Information Systems
An improved branch and bound algorithm for feature selection
Pattern Recognition Letters
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SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Hybrid Genetic Algorithms for Feature Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Estimating the detector coverage in a negative selection algorithm
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Dimensionality reduction using genetic algorithms
IEEE Transactions on Evolutionary Computation
V-detector: An efficient negative selection algorithm with "probably adequate" detector coverage
Information Sciences: an International Journal
EURASIP Journal on Advances in Signal Processing
BGSA: binary gravitational search algorithm
Natural Computing: an international journal
Non-singleton genetic fuzzy logic system for arrhythmias classification
Engineering Applications of Artificial Intelligence
A modified artificial immune network for feature extracting
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Artificial immune systems for epistasis analysis in human genetics
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Artificial immune systems perform valuable work when detecting epistasis in human genetic datasets
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Hybrid email spam detection model with negative selection algorithm and differential evolution
Engineering Applications of Artificial Intelligence
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The paper presents a new algorithm for feature selection and classification. The algorithm is based on an immune metaphor, and combines both negative and clonal selection mechanisms characteristic for B- and T-lymphocytes. The main goal of the algorithm is to select the best subset of features for classification. Two level evolution is used in the proposed system for detectors creation and feature selection. Subpopulations of evolving detectors (T-lymphocytes) are able to discover subsets of features well suited for classification. The subpopulations cooperate during evolution by means of a novel suppression mechanism which is compared to the traditional suppression mechanism. The proposed suppression method proved to be superior to the traditional suppression in both recognition performance and its ability to select the proper number of subpopulations dynamically. Some results in the task of ECG signals classification are presented. The results for binary and real coded T-lymphocytes are compared and discussed.