Non-linear dimensionality reduction techniques for unsupervised feature extraction
Pattern Recognition Letters
Interface Adaptation to Style of User-Computer Interaction
AH '00 Proceedings of the International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
A Hierarchical Multiclassifier System for Hyperspectral Data Analysis
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A User Pattern Learning Strategy for Managing Users' Mobility in UMTS Networks
IEEE Transactions on Mobile Computing
Classifier design with feature selection and feature extraction using layered genetic programming
Expert Systems with Applications: An International Journal
Network Protocol Verification by a Classifier Selection Ensemble
MCS '09 Proceedings of the 8th International Workshop on Multiple Classifier Systems
SVM decision boundary based discriminative subspace induction
Pattern Recognition
Application of NSGA-II to feature selection for facial expression recognition
Computers and Electrical Engineering
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
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In this paper, we propose a new feature extraction method for feedforward neural networks. The method is based on the recently published decision boundary feature extraction algorithm which is based on the fact that all the necessary features for classification can be extracted from the decision boundary. The decision boundary feature extraction algorithm can take advantage of characteristics of neural networks which can solve complex problems with arbitrary decision boundaries without assuming underlying probability distribution functions of the data. To apply the decision boundary feature extraction method, we first give a specific definition for the decision boundary in a neural network. Then, we propose a procedure for extracting all the necessary features for classification from the decision boundary. Experiments show promising results