The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining the results of several neural network classifiers
Neural Networks
The weighted majority algorithm
Information and Computation
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Combination of Multiple Classifiers Using Local Accuracy Estimates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
IEEE Transactions on Pattern Analysis and Machine Intelligence
On Fusers that Perform Better than Best Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Theoretical Study on Six Classifier Fusion Strategies
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
A Theoretical and Experimental Analysis of Linear Combiners for Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multi-sensor fusion: an Evolutionary algorithm approach
Information Fusion
Adaptive mixtures of local experts
Neural Computation
Boosting and other ensemble methods
Neural Computation
Ensemble confidence estimates posterior probability
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Learn++: an incremental learning algorithm for supervised neuralnetworks
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Switching between selection and fusion in combining classifiers: anexperiment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
The modified Dempster-Shafer approach to classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A target identification comparison of Bayesian and Dempster-Shafer multisensor fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiple network fusion using fuzzy logic
IEEE Transactions on Neural Networks
A Study of Semi-supervised Generative Ensembles
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
HUMANN-based systems for differential diagnosis of dementia using neuropsychological tests
INES'10 Proceedings of the 14th international conference on Intelligent engineering systems
A static evidential network for context reasoning in home-based care
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Computer Methods and Programs in Biomedicine
International Journal of Knowledge Discovery in Bioinformatics
Hi-index | 0.00 |
As the number of the elderly population affected by Alzheimer's disease (AD) rises rapidly, the need to find an accurate, inexpensive and non-intrusive diagnostic procedure that can be made available to community healthcare providers is becoming an increasingly urgent public health concern. Several recent studies have looked at analyzing electroencephalogram (EEG) signals through the use of wavelets and neural networks. While showing great promise, the final outcomes of these studies have been largely inconclusive. This is mostly due to inherent difficulty of the problem, but also - perhaps - due to inefficient use of the available information, as many of these studies have used a single EEG channel for the analysis. In this contribution, we describe an ensemble of classifiers based data fusion approach to combine information from two or more sources, believed to contain complementary information, for early diagnosis of Alzheimer's disease. Our emphasis is on sequentially generating an ensemble of classifiers that explicitly seek the most discriminating information from each data source. Specifically, we use the event related potentials recorded from the Pz, Cz, and Fz electrodes of the EEG, decomposed into different frequency bands using multiresolution wavelet analysis. The proposed data fusion approach includes generating multiple classifiers trained with strategically selected subsets of the training data from each source, which are then combined through a modified weighted majority voting procedure. The implementation details and the promising outcomes of this implementation are presented.