Extreme learning machine for classification over uncertain data

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 Due to various reasons, including imprecise measurement, network delay, outdated sources and sampling errors, data uncertainty is common and widespread in real-world applications, such as sensor database, location database, biometric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.