  In traditional ELM and its improved versions suffer from the problems of
outliers or noises due to overfitting and imbalance due to distribution. We
propose a novel hybrid adaptive fuzzy ELM(HA-FELM), which introduces a fuzzy
membership function to the traditional ELM method to deal with the above
problems. We define the fuzzy membership function not only basing on the
distance between each sample and the center of the class but also the density
among samples which based on the quantum harmonic oscillator model. The
proposed fuzzy membership function overcomes the shortcoming of the traditional
fuzzy membership function and could make itself adjusted according to the
specific distribution of different samples adaptively. Experiments show the
proposed HA-FELM can produce better performance than SVM, ELM, and RELM in text
classification.
