TY - JOUR
T1 - Chunk incremental learning for cost-sensitive hinge loss support vector machine
AU - Gu, Bin
AU - Quan, Xin
AU - Gu, Yunhua
AU - Sheng, Victor S.
AU - Zheng, Guansheng
N1 - Publisher Copyright:
© 2018
PY - 2018/11
Y1 - 2018/11
N2 - Cost-sensitive learning can be found in many real-world applications and represents an important learning paradigm in machine learning. The recently proposed cost-sensitive hinge loss support vector machine (CSHL-SVM) guarantees consistency with the cost-sensitive Bayes risk, and this technique provides better generalization accuracy compared to traditional cost-sensitive support vector machines. In practice, data typically appear in the form of sequential chunks, also called an on-line scenario. However, conventional batch learning algorithms waste a considerable amount of time under the on-line scenario due to re-training of a model from scratch. To make CSHL-SVM more practical for the on-line scenario, we propose a chunk incremental learning algorithm for CSHL-SVM, which can update a trained model without re-training from scratch when incorporating a chunk of new samples. Our method is efficient because it can update the trained model for not only one sample at a time but also multiple samples at a time. Our experimental results on a variety of datasets not only confirm the effectiveness of CSHL-SVM but also show that our method is more efficient than the batch algorithm of CSHL-SVM and the incremental learning method of CSHL-SVM only for a single sample.
AB - Cost-sensitive learning can be found in many real-world applications and represents an important learning paradigm in machine learning. The recently proposed cost-sensitive hinge loss support vector machine (CSHL-SVM) guarantees consistency with the cost-sensitive Bayes risk, and this technique provides better generalization accuracy compared to traditional cost-sensitive support vector machines. In practice, data typically appear in the form of sequential chunks, also called an on-line scenario. However, conventional batch learning algorithms waste a considerable amount of time under the on-line scenario due to re-training of a model from scratch. To make CSHL-SVM more practical for the on-line scenario, we propose a chunk incremental learning algorithm for CSHL-SVM, which can update a trained model without re-training from scratch when incorporating a chunk of new samples. Our method is efficient because it can update the trained model for not only one sample at a time but also multiple samples at a time. Our experimental results on a variety of datasets not only confirm the effectiveness of CSHL-SVM but also show that our method is more efficient than the batch algorithm of CSHL-SVM and the incremental learning method of CSHL-SVM only for a single sample.
KW - Chunk incremental learning
KW - Cost-sensitive learning
KW - Hinge loss
KW - Support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85048503210&partnerID=8YFLogxK
U2 - 10.1016/j.patcog.2018.05.023
DO - 10.1016/j.patcog.2018.05.023
M3 - Article
AN - SCOPUS:85048503210
SN - 0031-3203
VL - 83
SP - 196
EP - 208
JO - Pattern Recognition
JF - Pattern Recognition
ER -