Relative to the fuzzy membership as a function of distance between the point and its class center in feature space for some current fuzzy support vector machines

a new and more effective fuzzy membership as a function of affinity among samples is proposed for the measurement of the inaccuracy of samples. The fuzzy membership is defined by not only the relation between a sample and its cluster center

but also those among samples

which is described by the fuzzy connectedness among samples. The fuzzy membership based on the affinity among samples for support vector machine effectively distinguishes between support vectors and outliers or noises. Experimental results show that the fuzzy support vector machine

based on the affinity among samples is more robust than the traditional support vector machine

and fuzzy support vector machines taken by other two fuzzy memberships.