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Tree-based induction of decision list from survival data

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents an algorithm for induction of decision list from survival data. The algorithm uses a survival tree as the inner learner which is repeatedly executed in order to select the best rule at each iteration. The effectiveness of the algorithm was empirical tested for two implementations of survival trees on 15 benchmark datasets. The results show that proposed algorithm for survival decision list construction is able to induce more compact models than corresponding survival tree without the loss of the accuracy of predictions.
Rocznik
Tom
Strony
73--78
Opis fizyczny
Bibliogr. 28 poz., tab.
Twórcy
autor
  • Silesian University of Technology, ul. Akademicka 16, 44-100 Gliwice, Poland
Bibliografia
  • [1] AN, A., CERCONE, N., ELEM2: A learning system for more accurate classifications. In Advances in Artificial Intelligence, MERCER R., NEUFELD E., Eds., Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 1998, Vol. 1418 , pp. 426–441.
  • [2] AN, A., CERCONE, N., Rule quality measures for rule induction systems: Description and evaluation, Computational Intelligence, 2001, Vol. 17, No. 3, pp. 409–424.
  • [3] BAZAN J.G., OSMÓLSKI A., SKOWRON A., ŚLĘZAK D., SZCZUKA M.S., WRÓBLEWSKI J., Rough set approach to the survival analysis, Rough Sets and Current Trends in Computing, ALPIGINI J.J., PETERS, J.F., SKOWRONEK J., ZHONG E., Eds., Lecture Notes in Computer Science, Springer, 2002, Vol. 2475, pp. 522–529.
  • [4] BOU-HAMAD I., LAROCQUE D., BEN-AMEUR H., A review of survival tree,. Statistics Surveys, 2011, Vol. 5, pp. 44–71.
  • [5] BREIMAN L., FRIEDMAN J. ., OLSHEN R.A., STONE C.J., Classification and Regression Trees, Wadsworth, 1984.
  • [6] BRUHA I., TKADLEC J., Rule quality for multiple-rule classifier: Empirical expertise and theoretical methodology, Intelligent Data Analysis, 2003, Vol. 7, No. 2, pp. 99–124.
  • [7] CIAMPI A., THIFFAULT J., NAKACHE J., ASSELAIN B., Stratification by stepwise regression, correspondence analysis and recursive partition: a comparison of three methods of analysis for survival data with covariates, Computational statistics & data analysis, 1986, Vol. 4, No. 3, pp. 185–204.
  • [8] COX D., Regression models and life-tables, Journal of the Royal Statistical Society, Series B (Methodological) 1972, pp. 187–220.
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  • [11] FRANK E., WITTEN I., Generating accurate rule sets without global optimization, Proceedings of the Fifteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., 1998, pp. 144–151.
  • [12] GRAF E., SCHMOOR C., SAUERBREI W., SCHUMACHER M., Assessment and comparison of prognostic classification schemes for survival data, Statistics in medicine, 1999, Vol. 18, pp. 2529–2545.
  • [13] HOLMES G., HALL M., FRANK E., Generating rule sets from model trees, Twelfth Australian Joint Conference on Artificial Intelligence, Springer, 1999, pp. 1–12.
  • [14] HOTHORN T., HORNIK K., ZEILEIS A., Unbiased recursive partitioning: A conditional inference framework, Journal of Computational and Graphical Statistics, 2006, Vol. 15, No. 3, pp. 651–674.
  • [15] JANSSEN F., FÜRNKRANZ J., On the quest for optimal rule learning heuristics, Machine Learning, 2010, Vol. 78, No. 3, pp. 343–379.
  • [16] KAPLAN E.L., MEIER, P., Nonparametric estimation from incomplete observations, Journal of the American Statistical Association, 1958, Vol. 53, No. 282, pp. 457–481.
  • [17] KRONEK L.P., REDDY A., Logical analysis of survival data: prognostic survival models by detecting highdegree interactions in right-censored data, Bioinformatics 2008, Vol. 24, No. 16, pp. 248–253.
  • [18] LEBLANC M., CROWLEY J., Survival trees by goodness of split, Journal of the American StatisticalAssociation, 1993, pp. 457–467.
  • [19] PATTARAINTAKORN P., CERCONE N., A foundation of rough sets theoretical and computational hybrid intelligent system for survival analysis, Computers & Mathematics with Applications, 2008, Vol. 56, No. 7, pp. 1699–1708.
  • [20] PETO R., PETO J., Asymptotically efficient rank invariant test procedures, Journal of the Royal Statistical Society, Series A (General), 1972, pp. 185–207.
  • [21] R DEVELOPMENT CORE TEAM, R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, 2011. ISBN 3-900051-07-0.
  • [22] RIVEST R.L., Learning decision lists, Machine Learning, 1987, Vol. 2, pp. 229–246. 10.1007/BF00058680.
  • [23] SEGAL M., Regression trees for censored data, Biometrics, 1988, pp. 35–47.
  • [24] SIKORA M., Rule quality measures in creation and reduction of data rule models, Rough Sets and Current Trends in Computing, GRECO S., HATA Y., HIRANO S., INUIGUCHI M., MIYAMOTO S., NGUYEN H., SŁOWINSKI R., Eds., Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 2006, Vol. 4259, pp. 716–725.
  • [25] SIKORA M., SKOWRON A., WRÓBEL Ł., Rule quality measure-based induction of unordered sets of regression rules, Artificial Intelligence: Methodology, Systems and Applications, RAMSAY A.,. AGRE G., Eds., Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 2012, Vol. 7557, pp. 162–171.
  • [26] SIKORA M., WRÓBEL Ł., Data-driven adaptive selection of rules quality measures for improving the rules induction algorithm, Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, KUZNETSOV S., SLEZAK D., HEPTING D., MIRKIN B., Eds., Lecture Notes in Computer Science, Springer Berlin / Heidelberg, 2011, Vol. 6743, pp. 278–285.
  • [27] SIKORA M., WRÓBEL Ł., Data-driven adaptive selection of rule quality measures for improving rule induction and filtration algorithms, International Journal of General Systems (in press).
  • [28] THERNEAU T., ATKINSON B., RIPLEY B., RPART: Recursive partitioning, R package version 3.1-54, R port by Brian Ripley, 2012.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-PWA4-0027-0008
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