Extreme learning machine for classification over uncertain data
Extreme learning machine for classification over uncertain data
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DOI：
10.1016/j.neucom.2013.08.011
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发表时间：
2014-03
期刊：
影响因子：
6
通讯作者：
Yongjiao Sun;Ye Yuan;Guoren Wang
中科院分区：
文献类型：
--
作者：
Yongjiao Sun;Ye Yuan;Guoren Wang
传统的分类算法假设输入数据是准确的或精确的。由于测量不精确、网络延迟、数据源过时和采样误差等多种原因，数据不确定性在传感器数据库、位置数据库、生物识别信息系统等实际应用中普遍存在。尽管存在很多分类方法，但很少有方法能够解决数据库中不确定数据的分类问题。因此，在本文中，我们提出基于常规和优化ELM的分类算法来对不确定数据进行分类。首先，我们将每个不确定数据的实例视为学习的训练数据。然后，根据每个实例的学习结果计算任意类中不确定数据的概率。最后，使用基于界限的方法，我们实现了最终的分类。我们还基于 OS-ELM 和蒙特卡罗理论将所提出的算法扩展到分布式环境中的不确定数据分类。实验验证了我们提出的算法的性能。
Conventional classification algorithms assume that the input data is exact or precise. Due to various reasons, including imprecise measurement, network delay, outdated sources and sampling errors, data uncertainty is common and widespread in real-world applications, such as sensor database, location database, biometric information systems. Though there exist a lot of approaches for classification, few of them address the problem of classification over uncertain data in database. Therefore, in this paper, we propose classification algorithms based on conventional and optimized ELM to conduct classification over uncertain data. Firstly we view the instances of each uncertain data as the training data for learning. Then, the probabilities of uncertain data in any class are computed according to learning results of each instance. Finally, using a bound-based approach, we implement the final classification. We also extend the proposed algorithms to classification over uncertain data in a distributed environment based on OS-ELM and Monte Carlo theory. The experiments verify the performance of our proposed algorithms.