Six kinds of erythemato-squamous diseases have been common skin diseases, but the diagnosis of them has always been a problem. The quantitative data processing method is not suitable for erythemato-squamous data because they are categorical qualitative data. This paper proposed a new method based on group lasso penalized classification for the feature selection and classification for erythemato-squamous data with categorical qualitative data. The first categorical data of 33 dimensions were changed by the virtual code, and then 34th dimension age data were discretized and changed by the virtual code. Then the encoded data were grouped according to class group and variable group. Lastly Group Lasso penalized classification was executed. The classified accuracy of 10-fold cross validation was 98.88%±0.0023%. Compared with those of other method in the literature, this new method is simpler, and better for effect and efficiency, and has stronger interpretability and stronger stability.
Citation：
WANGJinjia, XUEFang. Group Lasso Penalized Classifier for Diagnosis of Diseases with Categorical Data. Journal of Biomedical Engineering, 2015, 32(5): 965-969. doi: 10.7507/1001-5515.20150172
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| 1. |
KARABATAK M, INCE M C. A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases[J].Expert Systems with Applications, 2009, 36(10):12500-12505.
|
| 2. |
XIE J, WANG C. Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases[J]. Expert Systems with Applications, 2011,38(5):5809-5815.
|
| 3. |
OZCIFT A, GULTEN A. A robust multiclass feature selection strategy based on rotation forest ensemble algorithm for diagnosis of erythemato-squamous diseases[J].Journal of Medical Systems, 2012, 36(2):941-949.
|
| 4. |
ABDI M J, GIVEKI D. Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules[J]. Engineering Application of Artificial Intelligence, 2013, 26(1):603-608.
|
| 5. |
OZCIFT A, GULTEN A. Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases[J]. Digital Signal Processing, 2013, 23(1):230-237.
|
| 6. |
SIMON N, FRIEDMAN J, HASTIE T. A Sparse-Group Lasso[J]. Journal of Computational and Graphical Statistics, 2013, 22(2):231-245.
|
| 7. |
陈为,沈则潜,陶煜波.数据可视化[M].北京:电子工业出版社,2014.
|
| 8. |
YUAN M, LIN Y. Model selection and estimation in regression with grouped variables[J]. Journal of the Royal Statistical Society, Series B, 2006, 68(1):49-67.
|
| 9. |
FRIEDMAN J, HASTIE T, HOFLING H, et al. Pathwise coordinate optimization[J]. The Annals of Applied Statistics, 2007, 1(2):302-332.
|
- 1. KARABATAK M, INCE M C. A new feature selection method based on association rules for diagnosis of erythemato-squamous diseases[J].Expert Systems with Applications, 2009, 36(10):12500-12505.
- 2. XIE J, WANG C. Using support vector machines with a novel hybrid feature selection method for diagnosis of erythemato-squamous diseases[J]. Expert Systems with Applications, 2011,38(5):5809-5815.
- 3. OZCIFT A, GULTEN A. A robust multiclass feature selection strategy based on rotation forest ensemble algorithm for diagnosis of erythemato-squamous diseases[J].Journal of Medical Systems, 2012, 36(2):941-949.
- 4. ABDI M J, GIVEKI D. Automatic detection of erythemato-squamous diseases using PSO-SVM based on association rules[J]. Engineering Application of Artificial Intelligence, 2013, 26(1):603-608.
- 5. OZCIFT A, GULTEN A. Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases[J]. Digital Signal Processing, 2013, 23(1):230-237.
- 6. SIMON N, FRIEDMAN J, HASTIE T. A Sparse-Group Lasso[J]. Journal of Computational and Graphical Statistics, 2013, 22(2):231-245.
- 7. 陈为,沈则潜,陶煜波.数据可视化[M].北京:电子工业出版社,2014.
- 8. YUAN M, LIN Y. Model selection and estimation in regression with grouped variables[J]. Journal of the Royal Statistical Society, Series B, 2006, 68(1):49-67.
- 9. FRIEDMAN J, HASTIE T, HOFLING H, et al. Pathwise coordinate optimization[J]. The Annals of Applied Statistics, 2007, 1(2):302-332.
