مشاهده مشخصات مقاله
Application of GA in Feature Optimization of Nearest Neighbor Classifiers
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Authors |
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M. Analoui
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M. Fadavi Amiri
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Conference |
دوازدهمین کنفرانس بینالمللی سالانه انجمن کامپیوتر ایران |
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Abstract |
The design of a pattern classifier includes an attempt to select, among a set of possible features, a minimum
subset of weakly correlated features that better discriminate the pattern classes. This is usually a difficult task in
practice, normally requiring the application of heuristic knowledge about the specific problem domain. The
selection and quality of the features representing each pattern have a considerable bearing on the success of
subsequent pattern classification. Feature extraction is the process of deriving new features from the original
features in order to reduce the cost of feature measurement, increase classifier efficiency, and allow higher
classification accuracy. Many current feature extraction techniques involve linear transformations of the original
pattern vectors to new vectors of lower dimensionality. While this is useful for data visualization and increasing
classification efficiency, it does not necessarily reduce the number of features that must be measured since each
new feature may be a linear combination of all of the features in the original pattern vector. In this paper a new
approach is presented to feature extraction in which feature selection, feature extraction, and classifier training
are performed simultaneously using a genetic algorithm. The genetic algorithm optimizes a vector of feature
weights, which are used to scale the individual features in the original pattern vectors in either a linear or a
nonlinear fashion. |
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قیمت |
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برای اعضای سایت : 100,000 Rial
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برای دانشجویان عضو انجمن : 20,000 Rial
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برای اعضای عادی انجمن : 40,000 Rial
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خرید مقاله
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