Machine Learning
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Using Correspondence Analysis to Combine Classifiers
Machine Learning
Performance analysis of pattern classifier combination by plurality voting
Pattern Recognition Letters
Fast probabilistic algorithms for hamiltonian circuits and matchings
STOC '77 Proceedings of the ninth annual ACM symposium on Theory of computing
GADT: A Probability Space ADT for Representing and Querying the Physical World
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Querying Imprecise Data in Moving Object Environments
IEEE Transactions on Knowledge and Data Engineering
Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Working Models for Uncertain Data
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
The Gauss-Tree: Efficient Object Identification in Databases of Probabilistic Feature Vectors
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Distributed classification in peer-to-peer networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Distributed Decision-Tree Induction in Peer-to-Peer Systems
Statistical Analysis and Data Mining
Cascade RSVM in Peer-to-Peer Networks
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Decision Trees for Uncertain Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Model-based clustering by probabilistic self-organizing maps
IEEE Transactions on Neural Networks
A comparative study on heuristic algorithms for generating fuzzydecision trees
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks
IEEE Transactions on Neural Networks
Dynamic ensemble extreme learning machine based on sample entropy
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Extreme Learning Machines (ELM 2011) Hangzhou, China, December 6 – 8, 2011
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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.