Original Contribution: Stacked generalization
Neural Networks
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal combinations of pattern classifiers
Pattern Recognition Letters
Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal linear combinations of neural networks
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Data Dependent Classifier Fusion for Construction of Stable Effective Algorithms
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 1 - Volume 01
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive fusion and co-operative training for classifier ensembles
Pattern Recognition
Data dependence in combining classifiers
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Proceedings of the 6th international conference on Multiple Classifier Systems
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Proceedings of the 6th international conference on Multiple Classifier Systems
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Risk function estimation for subproblems in a hierarchical classifier
Pattern Recognition Letters
| Hi-index | 0.01 |
In this paper, the data dependency of aggregation modules in multiple classifier system is being investigated. We first propose a new categorization scheme, in which combining methods are grouped into data-independent, implicitly data-dependent and explicitly data-dependent. It is argued that data-dependent approaches present the highest potential for improved performance. In this study, we intend to provide a comprehensive investigation of this argument and explore the impact of data dependency on the performance of multiple classifiers. We evaluate this impact based on two criteria, prediction accuracy and stability. In addition, we examine the effect of class imbalance and uneven data distribution on these two criteria. This paper presents the findings of an extensive set of comparative experiments. Based on the findings, it can be concluded that data-dependent aggregation methods are generally more stable and less sensitive to class imbalance. In addition, data-dependent methods exhibited superior or identical generalization ability for most of the data sets.