Classification of bone marrow cells based on ensemble of extreme learning machine
Chen Linwe · Computer Engineering and Applications Journal · 2015
Classification of bone marrow cells has important medical diagnostic significance. The training samples set extracted from the segmented images of bone marrow cells is used to train the extreme learning machine. Then this trained extreme learning machine automatically classifies the unknown bone marrow cells. For the instability of performance of single classifier, the ensemble of extreme learning machine algorithm based on cellular automata is proposed.The different training subsets are constructed by cellular automata strategy through sampling, then they are learned in parallel with multiple classifiers, finally the outputs are combined by majority voting. Experimental results show that this proposed algorithm has fast learning speed and gains high classification accuracy reached 97.33% without adjusting any parameters during run-time compared with BP neural networks and support vector machines. Moreover, it effectively solves the disadvantage of instability for the neural network classifier.