Skip to Main content Skip to Navigation
Conference papers

Entropy-Based Support Matrix Machine

Abstract : Support Vector Machine (SVM) cannot process imbalanced problem and matrix patterns. Thus, Fuzzy SVM (FSVM) is proposed to process imbalanced problem while Support Matrix Machine (SMM) is proposed to process matrix patterns. FSVM applies a fuzzy membership to each training pattern such that different patterns can make different contributions to the learning machine. However, how to evaluate fuzzy membership becomes the key point to FSVM. Although SMM can process matrix patterns, it still has no ability to process imbalanced problem. This paper adopts SMM as the basic and proposes an entropy-based support matrix machine for imbalanced data sets, i.e., ESMM. The contributions of ESMM are: (1) proposing an entropy-based fuzzy membership evaluation approach which enhances importance of certainty patterns, (2) guaranteeing importance of positive patterns and getting a more flexible decision surface. Experiments on real-world imbalanced data sets and matrix patterns validate the effectiveness of ESMM.
Document type :
Conference papers
Complete list of metadata

Cited literature [27 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Friday, June 22, 2018 - 10:43:06 AM
Last modification on : Friday, June 22, 2018 - 10:51:37 AM
Long-term archiving on: : Tuesday, September 25, 2018 - 1:02:24 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Changming Zhu. Entropy-Based Support Matrix Machine. 2nd International Conference on Intelligence Science (ICIS), Oct 2017, Shanghai, China. pp.200-211, ⟨10.1007/978-3-319-68121-4_21⟩. ⟨hal-01820906⟩



Record views


Files downloads