A weighted method based on Lars algorithm | Zendy

Lin Chen | Zendy; Shanxiong Chen | Zendy; Chunrong Chen | Zendy; Yuchen Zhu | Zendy

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A weighted method based on Lars algorithm

Author(s) -

Lin Chen,

Shanxiong Chen,

Chunrong Chen,

Yuchen Zhu

Publication year - 2017

Publication title -

iop conference series materials science and engineering

Language(s) - English

Resource type - Journals

eISSN - 1757-899X

pISSN - 1757-8981

DOI - 10.1088/1757-899x/242/1/012110

Subject(s) - lasso (programming language) , algorithm , linear regression , mathematics , regression , entropy (arrow of time) , set (abstract data type) , regression analysis , elastic net regularization , selection (genetic algorithm) , feature selection , variable (mathematics) , computer science , mathematical optimization , artificial intelligence , statistics , mathematical analysis , physics , quantum mechanics , world wide web , programming language

LASSO (Least Absolute Shrinkage and Selection Operator) is mainly used to realize variable selection, simultaneously its algorithm and some improved algorithm have gotten wide attention in many fields. To improve the accuracy of LASSO problem in calculating regression coefficients, this paper proposes a new algorithm based on LASR (Least Angle Regression) algorithm to change its approximation direction, which uses two weighted method (coefficient of variation method or entropy weight method) to calculate the weight of linear relationship between the independent and the dependent variables, so we can calculate a regression coefficients set of linear regression model. Compared with LARS algorithm, it can be proved that the improved algorithm mentioned in this paper has a more precise ability for prediction.

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