用于非监督特征选择的免疫克隆多目标优化算法

Immune clonal multi-objective algorithm for unsupervised feature selection

提出一种基于免疫克隆多目标优化算法的特征选择方法,先将非监督特征选择问题归结为多目标优化问题,然后构造相应的问题模型和目标函数.最后,采用免疫克隆多目标优化算法,通过增加相关特征的显著性,减小不相关特征的显著性来实现每个特征显著性的优化,达到特征选择的目的.UCI数据集的仿真实验表明,该算法降低了错误识别率,验证了其在非监督特征选择中的应用潜力.

The unsupervised feature selection is transferred into a multiobjective optimization problem, and the immune clonal selection algorithm for multi-objective optimization is applied to solve it. Firstly, the unsupervised feature selection problem is translated into multi-objective problem. Secondly, the model and the objective functions are constructed. Lastly, each feature of significance is optimized by increasing the significance of the related features and decreasing the significance of the unrelated features. Experimental results on UCI data sets show that the error recognition rate is decreased and that the effectiveness and potential of the method are validated.