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An Unsupervised Gene Selection Method Based on Multiobjective Ant Colony Optimization

Atefeh Naseri, Seyed Mohammad Hossein Hasheminejad

Abstract



The feature selection process can be defined as an optimization problem aimed to find all the relevant and informative features from the whole set of features. Most feature selection methods use class labels and are considered as supervised methods. But access to these labels is not possible in many real world problems. The identification of a subset of effective genes from the microarray data is one of these problems, which plays a key role in discovery and treatment of diseases. An unsupervised gene selection method based on the multiobjective ant colony optimization has been proposed in this study in which both univariate and multivariate techniques are used for evaluation of the relationship between genes to calculate the fitness function, so that with considering the correlation between genes it will have a better performance in addition to speed. This method was used to find the genes with the highest discriminative power and the minimum level of similarity and redundancy. According to the results, the accuracy of the proposed method has improved in most cases than other methods. On the other hand, it has reported that the proposed method has a low computational complexity, so it can be used for large-scale datasets.

Keywords


Gene Selection, Feature Selection, Multi-Objective Ant Colony Optimization (MOACO), Microarray Data.

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