Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
One-class svms for document classification
The Journal of Machine Learning Research
Learning to Decode Cognitive States from Brain Images
Machine Learning
Patterns of Activity in the Categorical Representations of Objects
Journal of Cognitive Neuroscience
One-class document classification via Neural Networks
Neurocomputing
A novelty detection approach to classification
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
fMRI analysis via one-class machine learning techniques
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Brain activation detection by neighborhood one-class SVM
Cognitive Systems Research
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In this paper, we demonstrate how one-class recognition of cognitive brain functions across multiple subjects can be performed at the 90% level of accuracy via an appropriate choices of features which can be chosen automatically. The importance of this work is that while one-class is often the appropriate classification setting for identifying cognitive brain functions, most work in the literature has focused on two-class methods. Our work extends one-class work by [1], where such classification was first shown to be possible in principle albeit with an accuracy of about 60%. The results are also comparable to work of various groups around the world e.g.[2], [3] and [4] which have concentrated on two-class classification. The strengthening in the feature selection was accomplished by the use of a genetic algorithm run inside the context of a wrapper approach around a compression neural network for the basic one-class identification. In addition, versions of one-class SVM due to [5] and [6] were investigated.