Open AccessLeast Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning
Author(s) -
Wenlu Ma,
Ran Liu
Publication year - 2021
Publication title -
informacinės technologijos ir valdymas
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.286
H-Index - 19
eISSN - 2335-884X
pISSN - 1392-124X
DOI - 10.5755/j01.itc.50.2.27752
Subject(s) - least squares support vector machine , support vector machine , kernel (algebra) , computer science , radial basis function kernel , relevance vector machine , artificial intelligence , robustness (evolution) , machine learning , kernel method , pattern recognition (psychology) , structured support vector machine , algorithm , mathematics , biochemistry , chemistry , combinatorics , gene
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.