| Presentation | 2010-06-14 Regularized Random Forest Method for Survival Analysis Toshio SHIMOKAWA, Mitsuhiro TSUJI, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | One of the important themes in survival analysis is to explore prognoses factors that influence survival time. Recently, the tree-structured method has been applied to evaluate covariates (e.g., Crowley, 2004); however, it is well known that this method has provides poor prediction model. This problem could be improved by modeling many trees in a linear combination, namely, ensemble learning. The ensemble learning method is actively studied in machine learning and statistics. The random forest is popular method that is applied in many fields (e.g., bioinformatics, environmentrics, and so on). In this presentation, we extended the random forest method to analyze survival data. Our proposed model has weight parameters, which are estimated by lasso (Tibshirani, 1996), for each tree. We call this method regurarized random survival forest method. Therefore, in regurarized random survival forest method, the trees (base learner) that strongly influence survival time will have large estimated parameter values; the parameters of trees that lack influence will be estimated as zero (pruning). Evaluation of regurarized random forest method, using simulated and real data sets, indicated that regurarized random survival forest method performs better than the ordinaly random survival forest method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | random forest / lasso / proportional hazard model / survival analysis |
| Paper # | IBISML2010-12 |
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| Conference Information | |
| Committee | IBISML |
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| Conference Date | 2010/6/7(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Information-Based Induction Sciences and Machine Learning (IBISML) |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Regularized Random Forest Method for Survival Analysis |
| Sub Title (in English) | |
| Keyword(1) | random forest |
| Keyword(2) | lasso |
| Keyword(3) | proportional hazard model |
| Keyword(4) | survival analysis |
| 1st Author's Name | Toshio SHIMOKAWA |
| 1st Author's Affiliation | Graduate School of Medicine and Engineering, University of Yamanashi() |
| 2nd Author's Name | Mitsuhiro TSUJI |
| 2nd Author's Affiliation | Faculty of Informatics, Kansai University |
| Date | 2010-06-14 |
| Paper # | IBISML2010-12 |
| Volume (vol) | vol.110 |
| Number (no) | 76 |
| Page | pp.pp.- |
| #Pages | 7 |
| Date of Issue | |