ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Finding Consistent Clusters in Data Partitions
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Path-Based Clustering for Grouping of Smooth Curves and Texture Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Clustering Using Evidence Accumulation
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 4 - Volume 4
Supervised fuzzy clustering for the identification of fuzzy classifiers
Pattern Recognition Letters
Cluster ensemble and its applications in gene expression analysis
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Ensemble Clustering in Medical Diagnostics
CBMS '04 Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems
Ensemble Methods in the Clustering of String Patterns
WACV-MOTION '05 Proceedings of the Seventh IEEE Workshops on Application of Computer Vision (WACV/MOTION'05) - Volume 1 - Volume 01
Combining multiple clustering systems
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Combining Multiple Clusterings Using Evidence Accumulation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A delivery framework for health data mining and analytics
ACSC '05 Proceedings of the Twenty-eighth Australasian conference on Computer Science - Volume 38
Hybrid Intelligent Systems: Selecting Attributes for Soft-Computing Analysis
COMPSAC '05 Proceedings of the 29th Annual International Computer Software and Applications Conference - Volume 01
Expert Systems with Applications: An International Journal
A Hybrid Data Mining Approach for Knowledge Extraction and Classification in Medical Databases
ISDA '07 Proceedings of the Seventh International Conference on Intelligent Systems Design and Applications
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
Multiobjective data clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Automated detection of tumors in mammograms using two segments for classification
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part I
An Evolutionary Approach to Multiobjective Clustering
IEEE Transactions on Evolutionary Computation
Simultaneous training of negatively correlated neural networks inan ensemble
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough–Fuzzy Collaborative Clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Ensemble-Based Incremental Learning Approach to Data Fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bagging and Boosting Negatively Correlated Neural Networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Ensemble Algorithms in Reinforcement Learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A combined neural network and decision trees model for prognosis of breast cancer relapse
Artificial Intelligence in Medicine
Model selection for a medical diagnostic decision support system: a breast cancer detection case
Artificial Intelligence in Medicine
On the effect of calibration in classifier combination
Applied Intelligence
Using TF-IDF to hide sensitive itemsets
Applied Intelligence
A belief classification rule for imprecise data
Applied Intelligence
Multi-level rough set reduction for decision rule mining
Applied Intelligence
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This paper presents a novel hybrid ensemble approach for classification in medical databases. The proposed approach is formulated to cluster extracted features from medical databases into soft clusters using unsupervised learning strategies and fuse the decisions using parallel data fusion techniques. The idea is to observe associations in the features and fuse the decisions made by learning algorithms to find the strong clusters which can make impact on overall classification accuracy. The novel techniques such as parallel neural-based strong clusters fusion and parallel neural network based data fusion are proposed that allow integration of various clustering algorithms for hybrid ensemble approach. The proposed approach has been implemented and evaluated on the benchmark databases such as Digital Database for Screening Mammograms, Wisconsin Breast Cancer, and Pima Indian Diabetics. A comparative performance analysis of the proposed approach with other existing approaches for knowledge extraction and classification is presented. The experimental results demonstrate the effectiveness of the proposed approach in terms of improved classification accuracy on benchmark medical databases.