关键词: Lasso, 特征选择, 均分式Lasso

Abstract: Lasso is a feature selection method based on 1-norm. Compared with the existing feature selection methods, it not only selects the features with strong correlation with the class label, but also has good stability, so that it has received considerable attention. However, with a high-dimensional and large dataset, like other feature selection methods, Lasso encounters the problems of large computation and overfitting. To address this issue, this paper proposes an improved Lasso method, called K-part Lasso. The K-part Lasso method divides the feature set into K-parts. It selects the features from each feature subset, and then merges the selected features into one feature set. It selects the features from this merged feature set. Experimental results show that the K-part Lasso method can effectively deal with the high-dimensional and large sample datasets.

Key words: Lasso, feature selection, K-part Lasso