Comparative study for multi-variable regression methods based on Laguerre polynomial and manifolds optimization. Issue 8 (16th August 2022)

• Record Type: Journal Article
• Authors: Li, Zibo; Yan, Zhengxiang; Li, Shicheng; Sun, Guangmin; Wang, Xin; Zhao, Dequn; Li, Yu; Liu, Xiucheng
• Is Part Of: Engineering computations. Volume 39:Issue 8(2022)
• Page Start: 3058
• Page End: 3082
• Publication Date: 2022-08-16
• Subjects: Polynomials regression -- Laguerre polynomials -- Manifolds optimization -- Feature selection -- Cascaded regression -- Genetic algorithm; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-12-2021-0766
• Languages: English
• ISSNs: 0264-4401

Abstract

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.