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...
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 general characteristics of the training data to select some features without involving any ...
Feature selection for high-dimensional data 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...
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...
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 ...
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 ...