In the data mining, the analysis of high-dimensional data is a critical but thorny research topic.TheLASSO (least absolute shrinkage\nand selection operator) algorithm avoids the limitations, which generally employ stepwise regression with information criteria to\nchoose the optimal model, existing in traditional methods. The improved-LARS (Least Angle Regression) algorithm solves the\nLASSO effectively. This paper presents an improved-LARS algorithm, which is constructed on the basis ofmultidimensional weight\nand intends to solve the problems in LASSO. Specifically, in order to distinguish the impact of each variable in the regression,\nwe have separately introduced part of principal component analysis (Part_PCA), Independent Weight evaluation, and CRITIC,\ninto our proposal.We have explored that these methods supported by our proposal change the regression track by weighted every\nindividual, to optimize the approach direction, aswell as the approach variable selection. As a consequence, our proposed algorithm\ncan yield better results in the promise direction. Furthermore, we have illustrated the excellent property of LARS algorithm based\non multidimensional weight by the Pima Indians Diabetes. The experiment results show an attractive performance improvement\nresulting from the proposed method, compared with the improved-LARS, when they are subjected to the same threshold value.
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