The Strength of Weak Learnability
Machine Learning
Machine Learning
Investigation of the CasCor family of learning algorithms
Neural Networks
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling with constructive backpropagation
Neural Networks
Incremental Learning with Respect to New Incoming Input Attributes
Neural Processing Letters
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Interference-less neural network training
Neurocomputing
Parallel growing and training of neural networks using output parallelism
IEEE Transactions on Neural Networks
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To improve the learning performance of neural network NN, this paper introduces an input attribute grouping based NN ensemble method. All of the input attributes are partitioned into exclusive groups according to the degree of inter-attribute promotion or correlation that quantifies the supportive interactions between attributes. After partitioning, multiple NNs are trained by taking each group of attributes as their respective inputs. The final classification result is obtained by integrating the results from each NN. Experimental results on several UCI datasets demonstrate the effectiveness of the proposed method.