Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning

Wenlu Ma,Han Liu

doi:10.5755/j01.itc.50.2.27752

Abstract

Least squares support vector machine (LSSVM) is a machine learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, and it has good performance in the data processingof small samples. The LSSVM model lacks sparsity and is unable to handle large-scale data problem, this articleproposes an LSSVM method based on mixture kernel learning and sparse samples. This algorithm reduces theinitial training set to a sub-dataset using a sparse selection strategy. It converts the single kernel function in theLSSVM model into a mixed kernel function and optimizes its parameters. The reduced sub-dataset is used fortraining LSSVM. Finally, a group of datasets in the UCI Machine Learning Repository were used to verify theeffectiveness of the proposed algorithm, which is applied to real-world power load data to achieve better fittingand improve the prediction accuracy.

Highlights

Support Vector Machine (SVM) [25] is one of the most important algorithms in the field of machine learning
To avoid randomness rameters of the Least squares support vector machine (LSSVM) model with a mixture kernel are optimized by IABC to improve the accuracy of reinSthteepe5xpOebrtiaminetnhtealmreosduelltws,iethacthhedabteastapnadrmamoedteelrsm. ust be run 10 times
LSSVM is an improved version of the SVM algorithm, but it lacks the sparsity of SVM, its single kernel function leads to low generalisation ability and accuracy in the case of a large dataset

Summary

Introduction

Support Vector Machine (SVM) [25] is one of the most important algorithms in the field of machine learning. SVM detection method has been widely employed on account of the advantages of small sample learning, good generalization ability and high accuracy At present, it is under the background of large samples in the era of big data. A support vector machine classification algorithm based on depth kernel theory was proposed that can be applied to large-scale data sets [21, 29]. An LSSVM regression al- the KKT conditions, the equivalent equations are obgorithm based on sparse samples and hybrid kernel tained by eliminating vector w and e : learning is proposed in this paper. Kernel types and parameters affect the prediction accuracy of the LSSVM algorithm training model, and the selection of kernel functions plays an important role in processing learning tasks.

Description of LSSVM

Sparse Subset Selection

SIABC-MixKLSSVM

Test Data and Evaluation Indicators

Experimental Results and Analysis

6.Conclusions