Aiming at the unrealistic assumptions of oracles and the difficult determination of control parameter in fault detection for alumina evaporation process

a modified cost-sensitive active learning(MCAL)method is proposed.The formal description of MCAL is given and unrealistic assumptions of oracles are relaxed.MCAL hybridizes particle swarm optimization(PSO)and cost-sensitive active learning(CAL)to improve the classification accuracy with multi-oracles and reduce the cost of labeling instances sampled.This optimization mechanism employs the continuous-valued PSO to optimize the control parameter to maximize the value of information of instance and minimize the cost of oracle.MACL is applied on the benchmark of alumina evaporation process for fault detection.Experimental results show that MCAL correctly selects the control parameter

obtains low misclassification cost

reduces labeling costs and increases the rate of fault detection.