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Simulating Classifier Outputs for Evaluating Parallel Combination Method
- Lecture Notes in computer Science, 4th International Workshop, Multiple Classifier Systems
, 2003
"... Abstract. The use of artificial outputs generated by a classifier simulator has recently emerged as a new trend to provide an underlying evaluation of classifier combination methods. In this paper, we propose a new method for the artificial generation of classifier outputs based on additional parame ..."
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Cited by 2 (2 self)
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Abstract. The use of artificial outputs generated by a classifier simulator has recently emerged as a new trend to provide an underlying evaluation of classifier combination methods. In this paper, we propose a new method for the artificial generation of classifier outputs based on additional parameters which provide sufficient diversity to simulate, for a problem of any number of classes and any type of output, any classifier performance. This is achieved through a two-step algorithm which first builds a confusion matrix according to desired behaviour and secondly generates, from this confusion matrix, outputs of any specified type. We provide the detailed algorithms and constraints to respect for the construction of the matrix and the generation of outputs. We illustrate on a small example the usefulness of the classifier simulator. 1
A New Classifier Simulator for Evaluating Parallel Combination Methods
- Proc. ICDAR 2003
, 2003
"... The use of artificial outputs generated by a classifier simulator has recently emerged as a new trend to provide an underlying evaluation of classifier combination methods. In this paper, we propose a new method for the artificial generation of classifier outputs based on additional parameters which ..."
Abstract
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Cited by 2 (0 self)
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The use of artificial outputs generated by a classifier simulator has recently emerged as a new trend to provide an underlying evaluation of classifier combination methods. In this paper, we propose a new method for the artificial generation of classifier outputs based on additional parameters which provide sufficient diversity to simulate, for a problem of any number of classes and any type of output, any classifier performance. This is achieved through a two-step algorithm which first builds a confusion matrix according to desired behaviour and secondly generates, from this confusion matrix, outputs of any specified type. We provide the detailed algorithms and constraints to respect for the construction of the matrix and the generation of outputs. We illustrate on a small example the usefulness of the classifier simulator. 1.
behavior of combination methods: the case
"... Building diverse classifier outputs to evaluate the ..."
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Combining Classifiers in Rotated Face Space
"... Face recognition is a very complex classification prob-lem due to nuisance variations in different conditions. Normally no single classifier can discriminate patterns well when unpredictable variations and a huge number of classes are involved. Combining multiple classifiers can improve discriminabi ..."
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Face recognition is a very complex classification prob-lem due to nuisance variations in different conditions. Normally no single classifier can discriminate patterns well when unpredictable variations and a huge number of classes are involved. Combining multiple classifiers can improve discriminability over the best single classifier. In this paper, we present a way to combine classifiers for face recognition problem based on APCA classifiers. The pro-posed combinator generates various classifiers by rotating various face spaces and fusing them by applying a weighted distance measure. The combined classifier is tested on the Asian Face Database with 856 images. Experiments show a 30 % reduction in classification error rate of our combined classifier and illustrates that combining classifiers from dif-ferent face spaces may perform better than those based on a single face space. 1
