Building actions from classification rules

[1]  Marie-Odile Cordier,et al.  Mining simulation data by rule induction to determine critical source areas of stream water pollution by herbicides , 2012 .

[2]  Marie-Odile Cordier,et al.  Simulating the effect of technical and environmental constraints on the spatio-temporal distribution of herbicide applications and stream losses , 2011 .

[3]  María José del Jesús,et al.  An overview on subgroup discovery: foundations and applications , 2011, Knowledge and Information Systems.

[4]  Mykola Pechenizkiy,et al.  Learning with Actionable Attributes: Attention -- Boundary Cases! , 2010, 2010 IEEE International Conference on Data Mining Workshops.

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

[6]  Shaofeng Liu,et al.  Integration of decision support systems to improve decision support performance , 2010, Knowledge and Information Systems.

[7]  Marie-Odile Cordier,et al.  A decision-oriented model to evaluate the effect of land use and agricultural management on herbicide contamination in stream water , 2009, Environ. Model. Softw..

[8]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[9]  Marie-Odile Cordier,et al.  A plot drainage network as a conceptual tool for the spatial representation of surface flow pathways in agricultural catchments , 2009, Comput. Geosci..

[10]  Véronique Masson,et al.  From Classification Rules to Action Recommendations , 2008 .

[11]  Véronique Masson,et al.  Symbolic learning of relationships between agricultural activities and water quality from simulations for decision support , 2008 .

[12]  Zbigniew W. Ras,et al.  Action Rules Discovery, a New Simplified Strategy , 2006, ISMIS.

[13]  Patrick J. Hayes,et al.  Primitive Intervals versus Point-Based Intervals: Rivals or Allies? , 2006, Comput. J..

[14]  Ke Wang,et al.  Mining patterns that respond to actions , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[15]  Weimin Xiao,et al.  Opportunity map: a visualization framework for fast identification of actionable knowledge , 2005, CIKM '05.

[16]  Véronique Masson,et al.  A Distance-Based Approach for Action Recommendation , 2005, ECML.

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

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

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

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

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

[22]  Marie-Odile Cordier,et al.  SACADEAU: A Decision-Aid System to improve Stream-Water Quality , 2005 .

[23]  Frédérick Garcia,et al.  A machine learning approach for evaluating the impact of land use and management practices on streamwater pollution by pesticides , 2005 .

[24]  Peter A. Flach,et al.  Decision Support Through Subgroup Discovery: Three Case Studies and the Lessons Learned , 2004, Machine Learning.

[25]  JOHANNES FÜRNKRANZ,et al.  Separate-and-Conquer Rule Learning , 1999, Artificial Intelligence Review.

[26]  Peter Clark,et al.  The CN2 Induction Algorithm , 1989, Machine Learning.

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

[28]  Zbigniew W. Ras,et al.  Discovering Semantic Inconsistencies to Improve Action Rules Mining , 2003, IIS.

[29]  Zbigniew W. Ras,et al.  Discovering Extended Action-Rules (System DEAR) , 2003, IIS.

[30]  Yuval Elovici,et al.  A decision-theoretic approach to data mining , 2003, IEEE Trans. Syst. Man Cybern. Part A.

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

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

[33]  Wynne Hsu,et al.  Identifying non-actionable association rules , 2001, KDD '01.

[34]  Donato Malerba,et al.  Comparing Dissimilarity Measures for Symbolic Data Analysis , 2001 .

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

[36]  Ian H. Witten,et al.  Generating Accurate Rule Sets Without Global Optimization , 1998, ICML.

[37]  Gediminas Adomavicius,et al.  Discovery of Actionable Patterns in Databases: the Action Hierarchy Approach , 1997, KDD.

[38]  Abraham Silberschatz,et al.  What Makes Patterns Interesting in Knowledge Discovery Systems , 1996, IEEE Trans. Knowl. Data Eng..

[39]  Janusz Zalewski,et al.  Rough sets: Theoretical aspects of reasoning about data , 1996 .

[40]  Michèle Sebag,et al.  Delaying the Choice of Bias: A Disjunctive Version Space Approach , 1996, ICML.

[41]  Gregory Piatetsky-Shapiro,et al.  The interestingness of deviations , 1994 .

[42]  Manabu Ichino,et al.  Generalized Minkowski metrics for mixed feature-type data analysis , 1994, IEEE Trans. Syst. Man Cybern..

[43]  Michael J. Pazzani,et al.  HYDRA: A Noise-tolerant Relational Concept Learning Algorithm , 1993, IJCAI.

[44]  Jerzy W. Grzymala-Busse,et al.  LERS-A System for Learning from Examples Based on Rough Sets , 1992, Intelligent Decision Support.

[45]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[46]  Peter Clark,et al.  Rule Induction with CN2: Some Recent Improvements , 1991, EWSL.

[47]  J. Ross Quinlan,et al.  Generating Production Rules from Decision Trees , 1987, IJCAI.

[48]  Nada Lavrac,et al.  The Multi-Purpose Incremental Learning System AQ15 and Its Testing Application to Three Medical Domains , 1986, AAAI.

[49]  Tom M. Mitchell,et al.  Generalization as Search , 2002 .

[50]  James F. Allen An Interval-Based Representation of Temporal Knowledge , 1981, IJCAI.

[51]  Ryszard S. Michalski,et al.  AQVAL/1--Computer Implementation of a Variable-Valued Logic System VL1 and Examples of its Application to Pattern Recognition , 1973, IJCAI 1973.