Today, with the increase of data dimensions, many challenges are faced in many contexts including machine learning, informatics, and medicine. However, reducing data dimension can be considered as a basic method in handling high-dimensional data, because by reducing dimensions, applying many of the...
Motivated by the problem of identifying correlations between genes or features of two related biological systems, we propose a model of \\emph{feature selection} in which only a subset of the predictors $X_t$ are dependent on the multidimensional variate $Y$, and the remainder of the ...
Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution Lei Yu leiyu@asu.edu Huan Liu hliu@asu.edu Department of Computer Science & Engineering, Arizona State University, Tempe, AZ 85287-5406, USA Abstract Feature selection, as a preprocessing step to machine learning...
lter solution is suitable for high dimensional data, and then review some recent e?orts in feature selection for high dimensional data. Feature selection algorithms can broadly fall into the ?lter model or the wrapper model (Das, 2001; Kohavi & John, 1997). The ?lter model relies on ...
Yu, L., and Liu, H.: Feature Selection for High-dimensional Data, A Fast Correlation- based Filter Solution. In 20th International Conference on ... V Bolón-Canedo,N Sánchez-Maroño,A Alonso-Betanzos - 《Progress in Artificial Intelligence》 被引量: 63发表: 2016年 A REVIEW ON CLUSTERI...
This paper offers a comprehensive approach to feature selection in the scope of classification problems, explaining the foundations, real application problems and the challenges of feature selection in the context of high-dimensional data. First, we focus on the basis of feature selection, providing ...
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Embedded feature selection; Sparsity; Regularization; Class-specific feature selection; High dimensional datasets; 机译:嵌入式特征选择;稀疏性;正则化;特定类特征选择;高维数据集; 入库时间 2022-08-17 13:30:27 相似文献 外文文献 中文文献 专利 1. Classification and Feature Selection Method for Medical...
The classes in thesklearn.feature_selectionmodule can be used for feature selection/dimensionality reduction on sample sets, either to improve estimators’ accuracy scores or to boost their performance on very high-dimensional datasets. Removing features with low variance --- 变异度选择法 ...
A feature subset selection technique for high dimensional data using symmetric uncertainty. J. Data Anal. Inf. Process. 2014, 2, 95-105. [CrossRef]Singh, B., Kushwaha, N., Vyas, O.P.: A Feature Subset Selection Technique for High Dimensional Data Using Symmetric Uncertainty. J. Data ...