Separate-and-conquer survival action rule learning

[1]  Vasiliy Krivtsov,et al.  Attention-based deep survival model for time series data , 2022, Reliab. Eng. Syst. Saf..

[2]  Tan N. Nguyen,et al.  Explainable artificial intelligence: a comprehensive review , 2021, Artificial Intelligence Review.

[3]  Zbigniew W. Ras,et al.  How to raise artwork prices using action rules, personalization and artwork visual features , 2021, Journal of Intelligent Information Systems.

[4]  Marek Sikora,et al.  SCARI: Separate and Conquer Algorithm for Action Rules and Recommendations Induction , 2021, Inf. Sci..

[5]  Pirooz Shamsinejad,et al.  GA2RM: A GA-Based Action Rule Mining Method , 2021, Int. J. Comput. Intell. Appl..

[6]  M. Kozielski,et al.  A Sensor Data-Driven Decision Support System for Liquefied Petroleum Gas Suppliers , 2021 .

[7]  Shamim Nemati,et al.  DeepAISE - An interpretable and recurrent neural survival model for early prediction of sepsis , 2021, Artif. Intell. Medicine.

[8]  Zbigniew W. Ras,et al.  NLP-Based Customer Loyalty Improvement Recommender System (CLIRS2) , 2021, Big Data Cogn. Comput..

[9]  Zbigniew W. Ras,et al.  Sentiment analysis of customer data , 2019, Web Intell..

[10]  Zbigniew W. Ras,et al.  Extraction of actionable knowledge to reduce hospital readmissions through patients personalization , 2019, Inf. Sci..

[11]  Ping Wang,et al.  Machine Learning for Survival Analysis , 2019, ACM Comput. Surv..

[12]  Marek Sikora,et al.  Bidirectional Action Rule Learning , 2018, ISCIS.

[13]  T. Vetter,et al.  Survival Analysis and Interpretation of Time-to-Event Data: The Tortoise and the Hare , 2018, Anesthesia and analgesia.

[14]  Tom Johnsten,et al.  A Multi-Objective Evolutionary Action Rule Mining Method , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[15]  Marek Sikora,et al.  GuideR: a guided separate-and-conquer rule learning in classification, regression, and survival settings , 2018, Knowl. Based Syst..

[16]  Zbigniew W. Ras,et al.  SARGS method for distributed actionable pattern mining using spark , 2017, 2017 IEEE International Conference on Big Data (Big Data).

[17]  Angelina A. Tzacheva,et al.  Action Rules for Sentiment Analysis on Twitter Data Using Spark , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[18]  Angelina A. Tzacheva,et al.  Discovery of Action Rules at Lowest Cost in Spark , 2017, 2017 IEEE International Conference on Data Mining Workshops (ICDMW).

[19]  Fabrizio Silvestri,et al.  Interpretable Predictions of Tree-based Ensembles via Actionable Feature Tweaking , 2017, KDD.

[20]  Marek Sikora,et al.  Learning rule sets from survival data , 2017, BMC Bioinformatics.

[21]  Yanchun Zhang,et al.  Mining Actionable Knowledge Using Reordering Based Diversified Actionable Decision Trees , 2016, WISE.

[22]  Angelina A. Tzacheva,et al.  MR - Random Forest Algorithm for Distributed Action Rules Discovery , 2016 .

[23]  Nassir Navab,et al.  Fast Training of Support Vector Machines for Survival Analysis , 2015, ECML/PKDD.

[24]  Yixin Chen,et al.  Optimal Action Extraction for Random Forests and Boosted Trees , 2015, KDD.

[25]  Ayman Hajja,et al.  Reduction of Readmissions to Hospitals Based on Actionable Knowledge Discovery and Personalization , 2015, BDAS.

[26]  Zbigniew W. Ras,et al.  Mining Surgical Meta-actions Effects with Variable Diagnoses' Number , 2014, ISMIS.

[27]  Alicja Wieczorkowska,et al.  Hierarchical object-driven action rules , 2014, Journal of Intelligent Information Systems.

[28]  Véronique Masson,et al.  Building actions from classification rules , 2012, Knowledge and Information Systems.

[29]  Denis Larocque,et al.  A review of survival trees , 2011 .

[30]  Johannes Fürnkranz,et al.  A review and comparison of strategies for handling missing values in separate-and-conquer rule learning , 2011, Journal of Intelligent Information Systems.

[31]  Monika Mielcarek,et al.  Higher CD34(+) and CD3(+) cell doses in the graft promote long-term survival, and have no impact on the incidence of severe acute or chronic graft-versus-host disease after in vivo T cell-depleted unrelated donor hematopoietic stem cell transplantation in children. , 2010, Biology of blood and marrow transplantation : journal of the American Society for Blood and Marrow Transplantation.

[32]  Zbigniew W. Ras,et al.  Action rule discovery from incomplete data , 2010, Knowledge and Information Systems.

[33]  Zbigniew W. Ras,et al.  Association Action Rules and Action Paths Triggered by Meta-actions , 2010, 2010 IEEE International Conference on Granular Computing.

[34]  Bojana Dalbelo Basic,et al.  Learning Bayesian networks from survival data using weighting censored instances , 2010, J. Biomed. Informatics.

[35]  Bojana Dalbelo Basic,et al.  Impact of censoring on learning Bayesian networks in survival modelling , 2009, Artif. Intell. Medicine.

[36]  Jan Rauch,et al.  Action Rules and the GUHA Method: Preliminary Considerations and Results , 2009, ISMIS.

[37]  Zbigniew W. Ras,et al.  Association Action Rules , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[38]  Zbigniew W. Ras,et al.  Action Rules Discovery without Pre-existing Classification Rules , 2008, RSCTC.

[39]  H. Ishwaran,et al.  Random survival forests , 2008, 0811.1645.

[40]  Anupama Reddy,et al.  Logical analysis of survival data: prognostic survival models by detecting high-degree interactions in right-censored data , 2008, ECCB.

[41]  Zbigniew W. Ras,et al.  Action Rule Extraction from a Decision Table: ARED , 2008, ISMIS.

[42]  Zbigniew W. Ras,et al.  ARAS: Action Rules Discovery Based on Agglomerative Strategy , 2007, MCD.

[43]  Salvatore Greco,et al.  Customer satisfaction analysis based on rough set approach , 2007 .

[44]  K. Hornik,et al.  Unbiased Recursive Partitioning: A Conditional Inference Framework , 2006 .

[45]  Ke Wang,et al.  Mining Actionable Patterns by Role Models , 2006, 22nd International Conference on Data Engineering (ICDE'06).

[46]  Zengyou He,et al.  Mining action rules from scratch , 2005, Expert Syst. Appl..

[47]  Zbigniew W. Ras,et al.  Mining for interesting action rules , 2005, IEEE/WIC/ACM International Conference on Intelligent Agent Technology.

[48]  Angelina A. Tzacheva,et al.  Action rules mining , 2005, Int. J. Intell. Syst..

[49]  Salvatore Greco,et al.  Measuring Attractiveness of Rules from the Viewpoint of Knowledge Representation, Prediction and Efficiency of Intervention , 2005, AWIC.

[50]  Stefan Vogt,et al.  Zinc concentration in esophageal biopsy specimens measured by x-ray fluorescence and esophageal cancer risk. , 2005, Journal of the National Cancer Institute.

[51]  Zengyou He,et al.  Data Mining for Actionable Knowledge: A Survey , 2005, ArXiv.

[52]  Salvatore Greco,et al.  Measuring expected effects of interventions based on decision rules , 2005, J. Exp. Theor. Artif. Intell..

[53]  Zbigniew W. Ras,et al.  Action rules discovery: system DEAR2, method and experiments , 2005, J. Exp. Theor. Artif. Intell..

[54]  J. Ball,et al.  Statistics review 12: Survival analysis , 2004, Critical care.

[55]  Lionel Tarassenko,et al.  Non‐linear survival analysis using neural networks , 2004, Statistics in medicine.

[56]  Qiang Yang,et al.  Postprocessing decision trees to extract actionable knowledge , 2003, Third IEEE International Conference on Data Mining.

[57]  S. Love,et al.  Survival Analysis Part II: Multivariate data analysis – an introduction to concepts and methods , 2003, British Journal of Cancer.

[58]  T G Clark,et al.  Survival Analysis Part I: Basic concepts and first analyses , 2003, British Journal of Cancer.

[59]  Qiang Yang,et al.  Mining case bases for action recommendation , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[60]  Qiang Yang,et al.  Mining optimal actions for profitable CRM , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[61]  Andrzej Skowron,et al.  Rough Set Approach to the Survival Analysis , 2002, Rough Sets and Current Trends in Computing.

[62]  Zbigniew W. Ras,et al.  Action-Rules: How to Increase Profit of a Company , 2000, PKDD.

[63]  E Graf,et al.  Assessment and comparison of prognostic classification schemes for survival data. , 1999, Statistics in medicine.

[64]  M Schumacher,et al.  Modelling the effects of standard prognostic factors in node-positive breast cancer , 1999, British Journal of Cancer.

[65]  E. Biganzoli,et al.  Feed forward neural networks for the analysis of censored survival data: a partial logistic regression approach. , 1998, Statistics in medicine.

[66]  D Faraggi,et al.  A neural network model for survival data. , 1995, Statistics in medicine.

[67]  W. Sauerbrei,et al.  Randomized 2 x 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. German Breast Cancer Study Group. , 1994, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[68]  P. Novotny,et al.  Prospective evaluation of prognostic variables from patient-completed questionnaires. North Central Cancer Treatment Group. , 1994, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[69]  M. LeBlanc,et al.  Survival Trees by Goodness of Split , 1993 .

[70]  S. P. Wright,et al.  Adjusted P-values for simultaneous inference , 1992 .

[71]  M. LeBlanc,et al.  Relative risk trees for censored survival data. , 1992, Biometrics.

[72]  Mark R. Segal,et al.  Regression Trees for Censored Data , 1988 .

[73]  D. Harrington.  A class of rank test procedures for censored survival data , 1982 .

[74]  C. Jenkins,et al.  Clinically unrecognized myocardial infarction in the Western Collaborative Group Study. , 1967, The American journal of cardiology.

[75]  E. Kaplan,et al.  Nonparametric Estimation from Incomplete Observations , 1958 .

[76]  Egill A. Fridgeirsson,et al.  Transformer-Based Deep Survival Analysis , 2021, SPACA.

[77]  F. Harrell.  Introduction to Survival Analysis , 2015 .

[78]  Sabine Van Huffel,et al.  Improved performance on high-dimensional survival data by application of Survival-SVM , 2011, Bioinform..

[79]  Zbigniew W. Ras,et al.  Mining E-Action Rules, System DEAR , 2008, Data Mining: Foundations and Practice.

[80]  Qiang Yang,et al.  Extracting Actionable Knowledge from Decision Trees , 2007, IEEE Transactions on Knowledge and Data Engineering.

[81]  P. Bühlmann,et al.  Survival ensembles. , 2006, Biostatistics.

[82]  R. Agrawal,et al.  Fast Algorithms for Mining Association Rules , 1998 .

[83]  K M Leung,et al.  Censoring issues in survival analysis. , 1997, Annual review of public health.

[84]  B. Brown.  Case studies in biometry , 1996 .

[85]  R A Kyle,et al.  "Benign" monoclonal gammopathy--after 20 to 35 years of follow-up. , 1993, Mayo Clinic proceedings.

[86]  D.,et al.  Regression Models and Life-Tables , 2022 .

[87]  P. Pattaraintakorn,et al.  Computers and Mathematics with Applications , 2022 .