CAJ | 학술논문

骨髓细胞的分类有重要的医学诊断意义。先对骨髓细胞图像分割和特征提取，用提取出来的训练集对极限学习机训练，再用该分类器对未知样本识别。针对单个分类器性能的不稳定，提出基于元胞自动机的极限学习机集成算法。通过元胞自动机抽样策略构建差异大的训练子集，多个分类器并行学习，多数投票法联合决策。实验结果表明，与BP、支持向量机比较，该算法基本无参数调整，学习速度快，分类精度高能达到97.33%，且有效克服了神经网络分类器不稳定的缺点。
골수세포적분류유중요적의학진단의의。선대골수세포도상분할화특정제취，용제취출래적훈련집대겁한학습궤훈련，재용해분류기대미지양본식별。침대단개분류기성능적불은정，제출기우원포자동궤적겁한학습궤집성산법。통과원포자동궤추양책략구건차이대적훈련자집，다개분류기병행학습，다수투표법연합결책。실험결과표명，여BP、지지향량궤비교，해산법기본무삼수조정，학습속도쾌，분류정도고능체도97.33%，차유효극복료신경망락분류기불은정적결점。
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 perfor-mance 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 par-allel with multiple classifiers, finally the outputs are combined by majority voting. Experimental results show that this pro-posed 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.