Discovering cluster-based local outliers
Pattern Recognition Letters
Estimating the Support of a High-Dimensional Distribution
Neural Computation
The challenge problem for automated detection of 101 semantic concepts in multimedia
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Knowledge transfer via multiple model local structure mapping
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Transfer learning from multiple source domains via consensus regularization
Proceedings of the 17th ACM conference on Information and knowledge management
Domain adaptation from multiple sources via auxiliary classifiers
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
Multi-source domain adaptation and its application to early detection of fatigue
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Detecting ECG abnormalities via transductive transfer learning
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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Recent advances in smart mobile device technology have resulted in global availability of portable computing devices capable of performing many complex functions. With the ultimate intent of promoting human's well-being, mobile device based arrhythmia detection (MAD) has attracted lots of attention recently. Without any guidance or supervision from experts, the performance of arrhythmia detection is usually unsatisfactory. Supervised learning can learn from labeled cardiac cycles to detect arrhythmias for each mobile device user if enough training data is provided. However, it is time-consuming, costly and sometimes impossible to let experts annotate enough training data for each user. To tackle this problem, we take advantage of publicly available and well annotated data to infer knowledge which can be treated as experts for MAD. To reduce the space usage of the framework, we extract from each source of labeled data an expert model, which consists of a task-independent individual characteristic vector and a task-related preference vector. Multiple experts are then integrated into an ensemble model for arrhythmia detection. Both space and time complexities of this proposed approach are theoretically analyzed and experimentally examined. To evaluate the performance of the method, we implement it on the MIT-BIH Arrhythmia Dataset and compare it with seven state-of-the-art methods in the area. Extensive experimental results show that the proposed algorithm outperforms all the baseline methods, which validates the effectiveness of the proposed algorithm in MAD.