Features Interaction Lasso for Liver Disease Classification / 生物医学工程学杂志
Journal of Biomedical Engineering
;
(6): 1227-1232, 2015.
Article
in Chinese
| WPRIM
| ID: wpr-357889
ABSTRACT
To solve the complex interaction problems of hepatitis disease classification, we proposed a lasso method (least absolute shrinkage and selection operator method) with feature interaction. First, lasso penalized function and hierarchical convex constraint were added to the interactive model which is newly defined. Then the model was solved with the convex optimal method combining Karush-Kuhn-Tucker (KKT) condition with generalized gradient descent. Finally, the sparse solution of the main effect features and interactive features were derived, and the classification model was implemented. The experiments were performed on two liver data sets and proved that features interaction contributed to the classification of liver diseases. The experimental results showed that the feature interaction lasso method was of strong explanatory ability, and its effectiveness and efficiency were superior to those of lasso, of all pair-wise lasso, support vector machine (SVM) method, K nearest neighbor (KNN) method, linear discriminant analysis (LDA) classification method, etc.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Algorithms
/
Cluster Analysis
/
Discriminant Analysis
/
Classification
/
Support Vector Machine
/
Liver Diseases
Limits:
Humans
Language:
Chinese
Journal:
Journal of Biomedical Engineering
Year:
2015
Type:
Article
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