PF-FELM: A Robust PCA Feature Selection for Fuzzy Extreme Learning Machine
PF-FELM: A Robust PCA Feature Selection for Fuzzy Extreme Learning Machine is a scholarly work, published in 2018 in ''IEEE Journal on Selected Topics in Signal Processing''. The main subjects of the publication include curse of dimensionality, ranking function, artificial intelligence, artificial neural network, feature, extreme learning machine, principal component analysis, machine learning, generalization, pattern recognition, facial recognition system, feature selection, computer science, fuzzy logic, dimensionality reduction, data mining, feature extraction, filter, and mathematics. The principal intent of this paper is to introduce a PCA-based optimal FSS for fuzzy extreme learning machine (PF-FELM) approach that is able to handle weighted classification problem.