Features Interaction Lasso for Liver Disease Classification_Journal of Biomedical Engineering

Authors：

 WANGJinjia , LUYang

School of Information Science and Engineer, Yanshan University, Qinhuangdao 066004, China;

Corresponding author：

WANGJinjia, Email: wjj@ysu.edu.cn

Keywords：

classification; features interaction; lasso; convex optimization; generalized gradient descent

DOI：

10.7507/1001-5515.20150218

Video：

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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.

Citation： WANGJinjia, LUYang. Features Interaction Lasso for Liver Disease Classification. Journal of Biomedical Engineering, 2015, 32(6): 1227-1232. doi: 10.7507/1001-5515.20150218 Copy

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15.	GARCKE, MICHAEL G. Data mining with sparse grids using simplicial basis functions[C]//Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.
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1. LIN R H. An intelligent model for liver disease using data mining algorithm[J]. Global Journal of Computer Science and Technology, 2009, 47(1):53-62.
2. KARTHIK S, PRIYADARISHINI A, ANURADHA J, et al. Classification and rule extraction using rough set for diagnosis of liver disease and its types[J]. Advanced Applied Scientific Research, 2011, 2(3):334-345.
3. NESHAT M, YAGHOBI M, NAGHIBI M B, et al. Fuzzy expert system design for diagnosis of liver disorders[J]. Knowledge Acquisition and Modeling, 2008, 8(18):252-256.
4. NESHAT M, ZADEH A E. Hopfield neural network and fuzzy Hopfield neural network for diagnosis of liver disorders[J]. Intelligent System, 2010, 5(10):162-267.
5. LIN J J, CHANG P C. A particle swarm optimization based on classifier for liver disorders classification[J]. Computational Problem-Solving, 2010, 12(5):63-65.
6. POLAT K, SAHAN S, KODAZ H, et al. A new classification method to diagnosis liver disorders[J]. Artificial Immune System, 2005, 12(9):169-174.
7. CHIEN L J, ZHI P K, LEE U J. Smooth lasso for classification[J]. Applications of Artificial Intelligence, 2010, 9(18):241-246.
8. KARTHIK S, PRIYADARISHINI A, ANURADHA J, et al. Classification and rule extraction using rough set for diagnosis of liver disease and its types[J]. Adv Appl Sci Res, 2011, 2(3):334-345.
9. NESHAT M, MEHDI S, ADEL N, et al. Hepatitis disease diagnosis using hybrid case based reasoning and particle swarm optimization[J]. ISRN Artificial Intelligence, 2012, 12(3):1-6.
10. EKONG V, ONIBERE A, IMIANVAN A. Fuzzy cluster means system for the diagnosis of liver diseases[J]. Computer Science and Technology, 2011, 2(3):205-209.
11. LIN R H. An intelligent model for liver disease diagnosis[J]. Artif Intell Med, 2009, 47(1):53-62.
12. BIEN J, TAYLOR J, TIBSHIRANI R. A lasso for hierarchical interactions[J]. Ann Stat, 2013, 41(3):1111-1141.
13. TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. Journal of the Royal Statistical Society, 1996, 58(1):267-288.
14. FRIEDMAN J, HASTIE T, TIBSHIRANI B. Regularization paths for generalized linear models via coordinate descent[J]. J Stat Softw, 2010, 33(1):253-258.
15. GARCKE, MICHAEL G. Data mining with sparse grids using simplicial basis functions[C]//Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2001.
16. DOMENICONI C, PENG J, GUNOPULOS D. An adaptive metric machine for pattern classification[J]. Adv Neural Inf Process Syst, 2000, 13(1):458-464.
17. LIORA X, GOLDBERG D, TRAUS I, et al. Accuracy, parsimony and generality in evolutionary learning systems via multi-objective selection[J]. Learning Classifier Systems, 2003, 2661(1):118-142.

Journal of Biomedical Engineering

Features Interaction Lasso for Liver Disease Classification

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