噪音数据的属性选择算法

正则化属性选择算法减小噪音数据影响的效果不佳,而且样本空间的局部结构几乎没有被考虑,在将样本映射到属性子空间后,样本之间的联系与原空间不一致,导致数据挖掘算法的效果不能令人满意.提出一个抗噪音属性选择方法,可以有效地解决传统算法的这两个缺陷.该方法首先采用自步学习的训练方式,这不仅能大幅度降低离群点进入训练的可能性,而且有利于模型的快速收敛;然后,采用加入l_2,1正则项的回归学习器进行嵌入式属性选择,兼顾“求得稀疏解”和“解决过拟合”,使模型更稳健;最后,融合局部保留投影的技术,将其投影矩阵转换成模型的回归参数矩阵,在属性选择的同时保持样本之间的原有局部结构.采用一系列基准数据集合测试该算法,在aCC和aRMSE上的实验结果,表明了该属性选择方法的有效性.

Feature Selection Algorithm for Noise Data

The regularization feature selection algorithm is not effective in reducing the impact of noisy data. Moreover, the local structure of the sample space is hardly considered. After the samples are mapped to the feature subspace, the relationship between samples is inconsistent with the original space, resulting in unsatisfactory results of the data mining algorithm. This study proposes an anti-noise feature selection method that can effectively solve these two shortcomings of traditional algorithms. This method first uses a self-paced learning training method, which not only greatly reduces the possibility of outliers entering training, but also facilitates the rapid convergence of the model. Then, a regression learner with regular terms is used to select the embedded features, taking into account the "sparse solution" and "solving over-fitting" to make the model more robust. Finally, the technique of locality preserving projections is integrated, and its projection matrix is transformed into the regression parameter matrix of the model, while maintaining the original local structure between the samples while selecting the features. Some experiments are conducted for evaluating the algorithm with a series of benchmark data sets. Experimental results show the effectiveness of the proposed algorithm in term of the aCC and aRMSE.