Fuzzy support vector machines (FSVMs) are known for their excellent antinoise performance, but there is no general rule when the fuzzy membership function (FMF) is set up. A novel FSVM based on hyperbolas optimized by the quantum-inspired gravitational search algorithm (QGSH-FSVM) is proposed to handle this question. In the proposed QGSH-FSVM, the FMF is defined by two disparate hyperbolas, whose eccentricities are optimized by the quantum-inspired gravitational search algorithm. A variable called diversity, revealing the percentage of a sample in different classes, is proposed to distinguish outliers or noises from valid samples. Experimental results confirm that the QGSH-FSVM is able to provide the best solutions to different situations by optimizing its eccentricities. The traditional support vector machine and the FSVM based on affinity or the distance between a sample and its cluster center, however, can only succeed in some particular problems while failing in others.
Fuzzy support vector machine, fuzzy membership function, hyperbolas, eccentricities, diversity
NI, FENG; HE, YUZHU; and JIANG, FEI
"Fuzzy support vector machine based on hyperbolas optimized by the quantum-inspired gravitational search algorithm,"
Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 25:
4, Article 43.
Available at: https://journals.tubitak.gov.tr/elektrik/vol25/iss4/43