IEEE Transactions on Pattern Analysis and Machine Intelligence
Synthesizing High-Frequency Rules from Different Data Sources
IEEE Transactions on Knowledge and Data Engineering
Database classification for multi-database mining
Information Systems
Semisupervised learning from different information sources
Knowledge and Information Systems
Efficient Classification across Multiple Database Relations: A CrossMine Approach
IEEE Transactions on Knowledge and Data Engineering
Spectral feature selection for supervised and unsupervised learning
Proceedings of the 24th international conference on Machine learning
Synthesizing heavy association rules from different real data sources
Pattern Recognition Letters
Top 10 algorithms in data mining
Knowledge and Information Systems
Stable feature selection via dense feature groups
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Design and evaluation of a hybrid sensor network for cane toad monitoring
ACM Transactions on Sensor Networks (TOSN)
Mining globally interesting patterns from multiple databases using kernel estimation
Expert Systems with Applications: An International Journal
Heterogeneous source consensus learning via decision propagation and negotiation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Multiple information sources cooperative learning
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Cross-Domain Learning from Multiple Sources: A Consensus Regularization Perspective
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams
ICDM '10 Proceedings of the 2010 IEEE International Conference on Data Mining
Expert Systems with Applications: An International Journal
Shell-neighbor method and its application in missing data imputation
Applied Intelligence
View determinacy for preserving selected information in data transformations
Information Systems
CLAP: Collaborative pattern mining for distributed information systems
Decision Support Systems
Improving data quality by source analysis
Journal of Data and Information Quality (JDIQ)
Image classification by multimodal subspace learning
Pattern Recognition Letters
Incremental Detection of Inconsistencies in Distributed Data
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
m-SNE: Multiview Stochastic Neighbor Embedding
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nearest neighbor selection for iteratively kNN imputation
Journal of Systems and Software
Pairwise constraints based multiview features fusion for scene classification
Pattern Recognition
Divergence-based feature selection for separate classes
Neurocomputing
Feature selection for high-dimensional imbalanced data
Neurocomputing
Quality of Information Based Data Selection and Transmission in Wireless Sensor Networks
RTSS '12 Proceedings of the 2012 IEEE 33rd Real-Time Systems Symposium
On Similarity Preserving Feature Selection
IEEE Transactions on Knowledge and Data Engineering
Transfer across Completely Different Feature Spaces via Spectral Embedding
IEEE Transactions on Knowledge and Data Engineering
Large-margin multi-view Gaussian process for image classification
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Mining stable patterns in multiple correlated databases
Decision Support Systems
Multiview Hessian discriminative sparse coding for image annotation
Computer Vision and Image Understanding
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Multiple information sources for the same set of objects can provide different representations, and combining their advantages may improve the predictive power for a given task. However, it is noticeable that some sources might be irrelevant or redundant. Thus, it is meaningful to select a set of good information sources that could help improve the learning performance, and very little work has been reported on this topic. In this paper, we first identify the two aspects of quality of information, source significance and source redundancy. In particular, significance represents the degree to which an information source contributes to the classification, and redundancy implies the information overlap among different information sources. We then propose a metric that combines neighborhood mutual information with a Max-Significance-Min-Redundancy algorithm, allowing us to select a compact set of superior information sources for classification learning. Extensive experiments show that the metric is very helpful in finding good information sources, and that the proposed method outperforms many other methods.