Series Info:INFOS2010 - 2010 7th International Conference on Informatics and Systems
Type:Conference Paper
Keywords:Classifier combination
,
Fuzzy gaussian classifier
,
Fuzzy K-nearest neighbors
,
K-nearest neighbors
,
Multi-layer perceptron
,
Benchmark data
,
Classification system
,
Classifier combination
,
Decision template
,
Fusion methods
,
Fuzzy integral
,
Fuzzy K-nearest neighbor classifier
,
Gaussian classifier
,
Gaussian model
,
K-nearest neighbor classifier
,
K-nearest neighbors
,
Multi layer perceptron
,
Multiple classifier systems
,
Product combinations
,
Fuzzy control
,
Gaussian distribution
,
Information science
,
Learning systems
,
Membership functions
,
Pattern recognition systems
,
Text processing
,
Classifiers
Abstract:
In the field of pattern recognition multiple classifier systems based on the combination of outputs from different classifiers have been proposed as a method of high performance classification systems. The objective of this work is to develop a fuzzy Gaussian classifier for combining multiple learners, we use a fuzzy Gaussian model to combine the outputs obtained from K-nearest neighbor classifier (KNN), Fuzzy K-nearest neighbor classifier and Multi-layer Perceptron (MLP) and then compare the results with Fuzzy Integral, Decision Templates, Weighted Majority, Majority Na�ve Bayes, Maximum, Minimum, Average and Product combination methods. Results on two benchmark data sets show that the proposed fusion method outperforms a wide variety of existing classifier combination methods.
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