基于实例和标签的分类任务特征选择方法

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Qingcheng Fan, Sicong Liu, Chunjiang Zhao, Shuqin Li
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引用次数: 0

摘要

特征选择在分类任务中至关重要,因为它有助于提取相关信息,同时减少冗余。本文提出了一种同时考虑实例关联和标签关联的新方法。采用最小二乘法计算各特征与目标变量之间的线性关系,得到相关系数。选择相关系数高的特征。与传统方法相比,我们的方法有两个优点。首先,从大量特征集中有效地选取与目标变量高度相关的特征,降低数据维数,提高分析建模效率;其次,我们的方法考虑了特征之间的标签相关性,提高了所选特征的准确性和后续模型的性能。在三个数据集上的实验结果表明,我们的方法在选择高相关系数的特征方面是有效的,从而获得了更好的模型性能。值得注意的是,我们的方法在高级分类器lightGBM上实现了3.2%的最小精度提高,超过了其他特征选择方法。综上所述,我们提出的基于实例和标签关联的分类方法,为分类问题提供了一个合适的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Instance- and Label-Based Feature Selection Method in Classification Tasks
Feature selection is crucial in classification tasks as it helps to extract relevant information while reducing redundancy. This paper presents a novel method that considers both instance and label correlation. By employing the least squares method, we calculate the linear relationship between each feature and the target variable, resulting in correlation coefficients. Features with high correlation coefficients are selected. Compared to traditional methods, our approach offers two advantages. Firstly, it effectively selects features highly correlated with the target variable from a large feature set, reducing data dimensionality and improving analysis and modeling efficiency. Secondly, our method considers label correlation between features, enhancing the accuracy of selected features and subsequent model performance. Experimental results on three datasets demonstrate the effectiveness of our method in selecting features with high correlation coefficients, leading to superior model performance. Notably, our approach achieves a minimum accuracy improvement of 3.2% for the advanced classifier, lightGBM, surpassing other feature selection methods. In summary, our proposed method, based on instance and label correlation, presents a suitable solution for classification problems.
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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