On ordered weighted averaging aggregation operators in multicriteria decisionmaking
IEEE Transactions on Systems, Man and Cybernetics
A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Techniques for automatically correcting words in text
ACM Computing Surveys (CSUR)
Fuzzy Sets and Systems
Floating search methods in feature selection
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
The ordered weighted averaging operators: theory and applications
The ordered weighted averaging operators: theory and applications
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
On the issue of obtaining OWA operator weights
Fuzzy Sets and Systems
Data mining: concepts and techniques
Data mining: concepts and techniques
Adaptive Intrusion Detection: A Data Mining Approach
Artificial Intelligence Review - Issues on the application of data mining
Customer Retention via Data Mining
Artificial Intelligence Review - Issues on the application of data mining
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Feature Selection Using Feature Similarity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
An improved branch and bound algorithm for feature selection
Pattern Recognition Letters
Feature Selection for Clustering - A Filter Solution
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
An introduction to variable and feature selection
The Journal of Machine Learning Research
Solving cluster ensemble problems by bipartite graph partitioning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Feature Selection for Unsupervised Learning
The Journal of Machine Learning Research
Toward Integrating Feature Selection Algorithms for Classification and Clustering
IEEE Transactions on Knowledge and Data Engineering
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An overview of methods for determining OWA weights: Research Articles
International Journal of Intelligent Systems
Clustering Ensembles: Models of Consensus and Weak Partitions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recursive and iterative OWA operators
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Soft Computing - A Fusion of Foundations, Methodologies and Applications
A survey of kernel and spectral methods for clustering
Pattern Recognition
Evolutionary model selection in unsupervised learning
Intelligent Data Analysis
Consensus unsupervised feature ranking from multiple views
Pattern Recognition Letters
Locality sensitive semi-supervised feature selection
Neurocomputing
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Computational Intelligence and Feature Selection: Rough and Fuzzy Approaches
Aggregation Functions: A Guide for Practitioners
Aggregation Functions: A Guide for Practitioners
New approaches to fuzzy-rough feature selection
IEEE Transactions on Fuzzy Systems
EVIDENCE DIRECTED GENERATION OF PLAUSIBLE CRIME SCENARIOS WITH IDENTITY RESOLUTION
Applied Artificial Intelligence
Disclosing false identity through hybrid link analysis
Artificial Intelligence and Law
MDAI'06 Proceedings of the Third international conference on Modeling Decisions for Artificial Intelligence
Some properties of the weighted OWA operator
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Using Stress Functions to Obtain OWA Operators
IEEE Transactions on Fuzzy Systems
Unsupervised feature evaluation: a neuro-fuzzy approach
IEEE Transactions on Neural Networks
Cluster-reliability-induced OWA operators
International Journal of Intelligent Systems
International Journal of Approximate Reasoning
A New Locally Weighted K-Means for Cancer-Aided Microarray Data Analysis
Journal of Medical Systems
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The intuition of data reliability has recently been incorporated into the main stream of research on ordered weighted averaging (OWA) operators. Instead of relying on human-guided variables, the aggregation behavior is determined in accordance with the underlying characteristics of the data being aggregated. Data-oriented operators such as the dependent OWA (DOWA) utilize centralized data structures to generate reliable weights, however. Despite their simplicity, the approach taken by these operators neglects entirely any local data structure that represents a strong agreement or consensus. To address this issue, the cluster-based OWA (Clus-DOWA) operator has been proposed. It employs a cluster-based reliability measure that is effective to differentiate the accountability of different input arguments. Yet, its actual application is constrained by the high computational requirement. This paper presents a more efficient nearest-neighbor-based reliability assessment for which an expensive clustering process is not required. The proposed measure can be perceived as a stress function, from which the OWA weights and associated decision-support explanations can be generated. To illustrate the potential of this measure, it is applied to both the problem of information aggregation for alias detection and the problem of unsupervised feature selection (in which unreliable features are excluded from an actual learning process). Experimental results demonstrate that these techniques usually outperform their conventional state-of-the-art counterparts.