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In traditional ELM and its improved versions suffer from the problems ofoutliers or noises due to overfitting and imbalance due to distribution. Wepropose a novel hybrid adaptive fuzzy ELM(HA-FELM), which introduces a fuzzymembership function to the traditional ELM method to deal with the aboveproblems. We define the fuzzy membership function not only basing on thedistance between each sample and the center of the class but also the densityamong samples which based on the quantum harmonic oscillator model. Theproposed fuzzy membership function overcomes the shortcoming of the traditionalfuzzy membership function and could make itself adjusted according to thespecific distribution of different samples adaptively. Experiments show theproposed HA-FELM can produce better performance than SVM, ELM, and RELM in textclassification.

Hybrid Adaptive Fuzzy Extreme Learning Machine for text classification