如何解决迁移学习中的负迁移问题并合理把握迁移的时机与方法,是影响迁移学习广泛应用的关键点.针对这个问题,提出一种基于相似度衡量机制的决策树自适应迁移方法 (Self-adaptive transfer for decision trees based on a similarity metric,STDT).首先,根据源任务数据集是否允许访问,自适应地采用成分预测概率或路径预测概率对决策树间的相似性进行判定,其亲和系数作为量化衡量关联任务相似程度的依据.然后,根据多源判定条件确定是否采用多源集成迁移,并将相似度归一化后依次分配给待迁移源决策树作为迁移权值.最后,对源决策树进行集成迁移以辅助目标任务实现决策.基于UCI机器学习库的仿真结果说明,与多源迁移加权求和算法(Weighted sum rule,WSR)和MS-TrAdaBoost相比,STDT能够在保证决策精度的前提下实现更为快速的迁移.
Coal mines require various kinds of machinery. The fault diagnosis of this equipment has a great impact on mine production. The problem of incorrect classification of noisy data by traditional support vector machines is addressed by a proposed Probability Least Squares Support Vector Classification Machine (PLSSVCM). Samples that cannot be definitely determined as belonging to one class will be assigned to a class by the PLSSVCM based on a probability value. This gives the classification results both a qualitative explanation and a quantitative evaluation. Simulation results of a fault diagnosis show that the correct rate of the PLSSVCM is 100%. Even though samples are noisy, the PLSSVCM still can effectively realize multi-class fault diagnosis of a roller bearing. The generalization property of the PLSSVCM is better than that of a neural network and a LSSVCM.
GAO Yang, WANG Xuesong, CHENG Yuhu, PAN Jie School of Information and Electrical Engineering, China University of Mining & Technology, Xuzhou 221116, China
In order to improve the generalization performance of support vector machine （SVM）,a support vector machine en...
Yong Ma is with the School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou,Jiangsu,221116,China.Xiaoxiao Kong is with the School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou,Jiangsu,221116,China.Xuesong Wang is with the School of Information and Electrical Engineering,China University of Mining and Technology,Xuzhou,Jiangsu,221116,China
