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Author:

Li, Zibo (Li, Zibo.) | Yan, Zhengxiang (Yan, Zhengxiang.) | Li, Shicheng (Li, Shicheng.) | Sun, Guangmin (Sun, Guangmin.) (Scholars:孙光民) | Wang, Xin (Wang, Xin.) | Zhao, Dequn (Zhao, Dequn.) | Li, Yu (Li, Yu.) | Liu, Xiucheng (Liu, Xiucheng.)

Indexed by:

EI Scopus SCIE

Abstract:

Purpose The purpose of this paper is to overcome the application limitations of other multi-variable regression based on polynomials due to the huge computation room and time cost. Design/methodology/approach In this paper, based on the idea of feature selection and cascaded regression, two strategies including Laguerre polynomials and manifolds optimization are proposed to enhance the accuracy of multi-variable regression. Laguerre polynomials were combined with the genetic algorithm to enhance the capacity of polynomials approximation and the manifolds optimization method was introduced to solve the co-related optimization problem. Findings Two multi-variable Laguerre polynomials regression methods are designed. Firstly, Laguerre polynomials are combined with feature selection method. Secondly, manifolds component analysis is adopted in cascaded Laguerre polynomials regression method. Two methods are brought to enhance the accuracy of multi-variable regression method. Research limitations/implications With the increasing number of variables in regression problem, the stable accuracy performance might not be kept by using manifold-based optimization method. Moreover, the methods mentioned in this paper are not suitable for the classification problem. Originality/value Experiments are conducted on three types of datasets to evaluate the performance of the proposed regression methods. The best accuracy was achieved by the combination of cascade, manifold optimization and Chebyshev polynomials, which implies that the manifolds optimization has stronger contribution than the genetic algorithm and Laguerre polynomials.

Keyword:

Cascaded regression Genetic algorithm Feature selection Polynomials regression Manifolds optimization Laguerre polynomials

Author Community:

  • [ 1 ] [Li, Zibo]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 2 ] [Yan, Zhengxiang]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 3 ] [Sun, Guangmin]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 4 ] [Zhao, Dequn]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 5 ] [Li, Yu]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
  • [ 6 ] [Li, Zibo]Classified Informat Carrier Safety Management Eng, Beijing, Peoples R China
  • [ 7 ] [Li, Shicheng]Classified Informat Carrier Safety Management Eng, Beijing, Peoples R China
  • [ 8 ] [Wang, Xin]Classified Informat Carrier Safety Management Eng, Beijing, Peoples R China
  • [ 9 ] [Li, Zibo]Beijing JingHang Res Inst Computat & Commun, Beijing, Peoples R China
  • [ 10 ] [Li, Shicheng]Beijing JingHang Res Inst Computat & Commun, Beijing, Peoples R China
  • [ 11 ] [Wang, Xin]Beijing JingHang Res Inst Computat & Commun, Beijing, Peoples R China
  • [ 12 ] [Liu, Xiucheng]Beijing Univ Technol, Coll Mech Engn & Appl Elect Technol, Beijing, Peoples R China

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Source :

ENGINEERING COMPUTATIONS

ISSN: 0264-4401

Year: 2022

Issue: 8

Volume: 39

Page: 3058-3082

1 . 6

JCR@2022

1 . 6 0 0

JCR@2022

ESI Discipline: ENGINEERING;

ESI HC Threshold:49

JCR Journal Grade:3

CAS Journal Grade:4

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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