Machine Learning
Pattern Recognition Letters
An approach to the automatic design of multiple classifier systems
Pattern Recognition Letters - Special issue on machine learning and data mining in pattern recognition
Proceedings of the First International Workshop on Multiple Classifier Systems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A Framework for Classifier Fusion: Is It Still Needed?
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
The ``Test and Select'' Approach to Ensemble Combination
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Complexity of Classification Problems and Comparative Advantages of Combined Classifiers
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Engineering multiversion neural-net systems
Neural Computation
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Comparison of Genetic Algorithm and Sequential Search Methods for Classifier Subset Selection
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
On the Effectiveness of Diversity When Training Multiple Classifier Systems
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
MLP, Gaussian Processes and Negative Correlation Learning for Time Series Prediction
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
A Study of Semi-supervised Generative Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
Robust prediction from multiple heterogeneous data sources with partial information
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Fast meta-models for local fusion of multiple predictive models
Applied Soft Computing
Comparing classifiers and metaclassifiers
ICDM'11 Proceedings of the 11th international conference on Advances in data mining: applications and theoretical aspects
Eigenclassifiers for combining correlated classifiers
Information Sciences: an International Journal
Empirical study on fusion methods using ensemble of RBFNN for network intrusion detection
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Using an ensemble of classifiers to audit a production classifier
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Using ensembles of binary case-based reasoners
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Lazy meta-learning: creating customized model ensembles on demand
WCCI'12 Proceedings of the 2012 World Congress conference on Advances in Computational Intelligence
Being SMART about failures: assessing repairs in SMART homes
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
Combining classifiers using nearest decision prototypes
Applied Soft Computing
Advanced Engineering Informatics
Learning to filter spam emails: An ensemble learning approach
International Journal of Hybrid Intelligent Systems
| Hi-index | 0.00 |
In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this paper, the problem of design of multiple classifier system is discussed. Six design methods based on the so-called "overproduce and choose" paradigm are described and compared by experiments. Although these design methods exhibited some interesting features, they do not guarantee to design the optimal multiple classifier system for the classification task at hand. Accordingly, the main conclusion of this paper is that the problem of the optimal MCS design still remains open.