Computational learning theory: an introduction
Computational learning theory: an introduction
Axiomatics for fuzzy rough sets
Fuzzy Sets and Systems
LEARNABLE EVOLUTION MODEL: Evolutionary Processes Guided by Machine Learning
Machine Learning - Special issue on multistrategy learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets and Data Mining: Analysis of Imprecise Data
Rough Sets and Data Mining: Analysis of Imprecise Data
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Rough-Fuzzy Hybridization: A New Trend in Decision Making
A comparative study of fuzzy rough sets
Fuzzy Sets and Systems
RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Accelerating the Convergence of Evolutionary Algorithms by Fitness Landscape Approximation
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Comparison Of Methods For Using Reduced Models To Speed Up Design Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Information Sciences—Informatics and Computer Science: An International Journal
Using approximations to accelerate engineering design optimization
Using approximations to accelerate engineering design optimization
An axiomatic characterization of a fuzzy generalization of rough sets
Information Sciences—Informatics and Computer Science: An International Journal
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A framework for evolutionary optimization with approximate fitnessfunctions
IEEE Transactions on Evolutionary Computation
On the generalization of fuzzy rough sets
IEEE Transactions on Fuzzy Systems
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No one can fool mother nature but we can learn from her, device many new methodologies through bio mimicry, since nature is the single and most complex system that has been field tested the longest. Being inspired by the mechanism through which our mother nature handling our blood sugar level, in this paper we proposed a new evolutionary algorithm for classification based on it. In this process we have identified that feature selection plays a vital role in deciding the performance behaviour of classifiers and an efficient feature or attribute selection can considerably augment the classification accuracy as well as reduces the run time of the algorithm. The paper describes the philosophy of optimum blood sugar controlling strategy being implemented in optimizing the feature selection and precision process of the classifier in the form of an algorithm. The efficiency of proposed algorithm is demonstrated experimentally on classifying the Iris dataset and Wine recognition dataset together with our laboratory generated humanoid robot dataset.