Reduction of rough set attribute based on immune clone selection

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This article has been updated. A Correction to this article was published on .

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The Original Article was published on

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The Original Article was published on

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This article has been updated

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Abstract

A novel attribute reduction approach of rough set based on immune clone selection is proposed. In this method, the approximation quality and attribute set were adopted as evolution object and antibody, respectively. On the basis of the inherent distribution within the immune response, the global optimization of the antibody was realized through parallel local optimization. Moreover, the diversity of the antibody population was maintained with the affinity maturation and renewal of the antibody. Thus, the stable multi-optimal solutions can be preserved. In addition, the machinery fault data were analyzed by this method, and the attribute reduction sets were obtained further to satisfy the demand of feature selection in machinery diagnosis.

Keywords

immune clone selection, rough set, attribute reduction, fault diagnosis

CHANGE HISTORY

A Correction to this paper has been published:
https://doi.org

The Retraction Note was updated to reflect reception of statements of disagreement from all authors.

This article has been retracted.Please see the Retraction Notice for more detail:
https://doi.org