Multi-layer Classifier and Its Application
Date Issued
2009
Date
2009
Author(s)
Wu, Hsin-Jung
Abstract
Fisher linear discriminant analysis is a common classification method. It classifies instances by a linear combination of attributes that simultaneously minimizes the differences within classes while maximizes the differences between classes. In this research, we propose a new classification method, which has a structure similar to the classification and regression trees (CART) , splitting instances layer by layer. The difference between this structure and CART is that this model classifies some instances into 1 or 2 classes in each layer with the unclassified instances left over to next layer for further classification. In addition, a linear combination of multiple attributes by the Fisher linear discriminant analysis can be selected as the classifier at each layer. In order to construct the classification method, we propose a systematic methodology to select relevant attributes and proper cutpoints. Addition of attributes into the model, will be evaluated by the full model’s performance to decide how the model grow. To avoid the over-fitting problem, we also propose a stopping criterion. To verify the model, we generate some simulation cases and use one real case to validate our model. The real case is “classification of thyroid nodules by quantitative features from ultrasound sonograph.” We will compare the new model’s result with the Fisher discriminant analysis and CART.
Subjects
Classification method
Fisher discriminant analysis
Classification and regression trees
Attribute selection
Cutpoint selection
Type
thesis
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