Classification approach for inertinite of coking coal based on an improved extreme learning machine

To improve the classification accuracy of inertinite macerals of coking coal and reduce the manual intervention in the training of classifier,a novel classification approach for the inertinite macerals of coking coal based on improved Extreme Learning Machine (ELM) is proposed. Firstly,according to the characteristics of inertinite macerals and difference between them,a 11-dimensional preliminary feature set about the intensity and texture,including six gray-level statistics based features as contrast,mean,standard deviation,deviation,consistency and kurtosis,and five gray level co-occurrence matrix based features as energy,entropy,moment,local smooth of coal microscopic images and maximum possibility,was built,and extracted with the principal component analysis ( PCA) method to reduce the dimension of feature space and remove redundancy. Secondly,a singular value decomposition ( SVD) method was introduced into the ELM,and the solution to calculate the output weight matrix of ELM was deduced by using SVD,an improved ELM was constructed. After improvement,the problem of parameter training for calculating the output weight matrix,which needs a large number of experiments to determine in conventional ELM,was solved,and the intelligent level of ELM was enhanced. Experimental results show that compared with SVM,the training and testing speed,the classification accuracy for the inertinite testing samples of improved ELM are obviously higher. Compared with the conventional ELM,the network training of classifier is convenient and faster,the number of hidden layer nodes is reduced about 40% , and the classification accuracy for testing samples is further improved, up to 96.7% .