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Feature selection often involves two conflicting objectives of minimizing the feature subset size and the maximizing the classification accuracy. In this paper, a multi-objective artificial bee colony (MOABC) framework is developed for feature selection in classification, and a new fuzzy mutual information based criterion is proposed to evaluate the relevance of feature subsets. Three new multi-objective...
Image segmentation is a key step in image analysis. The Gaussian Mixture Model (GMM) method based on image histograms is popular in image segmentation, but it is difficult to find good parameters of Gaussian models. A hybrid Particle Swarm Optimisation (PSO) algorithm has been used to effectively search the parameters of the models for image segmentation. However, the segmentation results from the...
Based on binary particle swarm optimisation (BPSO) and information theory, this paper proposes two new filter feature selection methods for classification problems. The first algorithm is based on BPSO and the mutual information of each pair of features, which determines the relevance and redundancy of the selected feature subset. The second algorithm is based on BPSO and the entropy of each group...
Evolutionary Art is a sub-field of Evolutionary Computing that involves creating interesting images using Evolutionary Techniques. Previously Genetic Programming has been used to create such images autonomously - that is, without a human in the loop. However, this work did not explore alternative fitness measures, consider colour in fitness or provide independent validation of results. Four fitness...
In this paper, Genetic programming (GP) is used to construct a new set of high level features based on the original attributes of a classification problem with the goal of improving the classification performance and reducing the dimensionality. A non-wrapper approach is taken and a new fitness function is proposed based on the Renyi entropy. The GP system uses a variable terminal pool which is constructed...
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