Open AccessSupport vector machine with quantile hyper-spheres for pattern classification
Author(s) -
Maoxiang Chu,
Xiaoping Li,
Rongfen Gong,
Jie Zhao
Publication year - 2019
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0212361
Subject(s) - quantile , decision boundary , hinge loss , cluster analysis , robustness (evolution) , pattern recognition (psychology) , support vector machine , margin (machine learning) , computer science , artificial intelligence , quadratic programming , mathematics , mathematical optimization , machine learning , statistics , biochemistry , chemistry , gene
This paper formulates a support vector machine with quantile hyper-spheres (QHSVM) for pattern classification. The idea of QHSVM is to build two quantile hyper-spheres with the same center for positive or negative training samples. Every quantile hyper-sphere is constructed by using pinball loss instead of hinge loss, which makes the new classification model be insensitive to noise, especially the feature noise around the decision boundary. Moreover, the robustness and generalization of QHSVM are strengthened through maximizing the margin between two quantile hyper-spheres, maximizing the inner-class clustering of samples and optimizing the independent quadratic programming for a target class. Besides that, this paper proposes a novel local center-based density estimation method. Based on it, ρ -QHSVM with surrounding and clustering samples is given. Under the premise of high accuracy, the execution speed of ρ -QHSVM can be adjusted. The experimental results in artificial, benchmark and strip steel surface defects datasets show that the QHSVM model has distinct advantages in accuracy and the ρ -QHSVM model is fit for large-scale datasets.