Enhanced Classification Based On Probabilistic Extreme Learning Machine In Wastewater Treatment Process

A binary classification method, Probabilistic Extreme Learning Machine (called P-ELM), is proposed to enhance the reliability of the classification of an unknown object. P-ELM method integrates ELM, density methods and Bayesian decision theory in order to take into account a priori probability of the process and the uncertainty of the ELM predictions. The P-ELM algorithm may inhibit uncertainty of the extreme learning machine prediction in the different trials of simulation due to the initialization of input weights and bias, which would damage the reliability of the classification for the new objects. Simulations results from a municipal wastewater treatment plant show that the proposed P-ELM binary classification method can provide the reliability of the classification for those samples near the boundaries of the classes and the reliability and accuracy outperform the ELM model. (C) 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of [CEIS 2011]