Extraction of qualitative behavior rules for industrial processes from reduced concept lattice

[1]  Weizhi Liao,et al.  A knowledge resources fusion method based on rough set theory for quality prediction , 2019, Comput. Ind..

[2]  Luis E. Zárate,et al.  Qualitative behavior rules for the cold rolling process extracted from trained ANN via the FCANN method , 2009, Eng. Appl. Artif. Intell..

[3]  Ming-Wen Shao,et al.  Attribute reduction in generalized one-sided formal contexts , 2017, Inf. Sci..

[4]  Camille Roth,et al.  Approaches to the Selection of Relevant Concepts in the Case of Noisy Data , 2010, ICFCA.

[5]  Radim Belohlávek,et al.  Impact of Boolean factorization as preprocessing methods for classification of Boolean data , 2014, Annals of Mathematics and Artificial Intelligence.

[6]  Ch. Aswanikumar,et al.  Concept lattice reduction using fuzzy K-Means clustering , 2010, Expert Syst. Appl..

[7]  Karell Bertet,et al.  The multiple facets of the canonical direct unit implicational basis , 2010, Theor. Comput. Sci..

[8]  Jozef Pócs,et al.  On concept reduction based on some graph properties , 2016, Knowl. Based Syst..

[9]  Radim Belohlávek,et al.  What is a Fuzzy Concept Lattice? II , 2011, RSFDGrC.

[10]  Wen-Xiu Zhang,et al.  Attribute reduction theory of concept lattice based on decision formal contexts , 2008, Science in China Series F: Information Sciences.

[11]  Jinhai Li,et al.  Incomplete decision contexts: Approximate concept construction, rule acquisition and knowledge reduction , 2013, Int. J. Approx. Reason..

[12]  Vilém Vychodil,et al.  Formal Concept Analysis With Background Knowledge: Attribute Priorities , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[13]  Bernard De Baets,et al.  Zoom-In/Zoom-Out Algorithms for FCA with Attribute Granularity , 2011, ISCIS.

[14]  Luis E. Zárate,et al.  Techniques for Training Sets Selection in the Representation of a Thermosiphon System Via ANN , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[15]  Sang-Eon Han,et al.  Lattice-theoretic contexts and their concept lattices via Galois ideals , 2016, Inf. Sci..

[16]  Veera Boonjing,et al.  A new case-based classification using incremental concept lattice knowledge , 2013, Data Knowl. Eng..

[17]  Ignacio Díaz Blanco,et al.  Visual analysis of a cold rolling process using a dimensionality reduction approach , 2013, Eng. Appl. Artif. Intell..

[18]  Sadok Ben Yahia,et al.  QualityCover: Efficient binary relation coverage guided by induced knowledge quality , 2016, Inf. Sci..

[19]  Pedro A. González-Calero,et al.  Formal concept analysis as a support technique for CBR , 2001, Knowl. Based Syst..

[20]  Ill-Soo Kim,et al.  A study on genetic algorithm to select architecture of a optimal neural network in the hot rolling process , 2004 .

[21]  Jean-François Boulicaut,et al.  Actionability and Formal Concepts: A Data Mining Perspective , 2008, ICFCA.

[22]  Sérgio M. Dias,et al.  A methodology for analysis of concept lattice reduction , 2017, Inf. Sci..

[23]  Zhang Wen-xiu,et al.  Attribute reduction theory and approach to concept lattice , 2005 .

[24]  Xia Wang,et al.  Relations of attribute reduction between object and property oriented concept lattices , 2008, Knowl. Based Syst..

[25]  Yan Wang,et al.  A hybrid approach of rough set and case-based reasoning to remanufacturing process planning , 2016, Journal of Intelligent Manufacturing.

[26]  Václav Snásel,et al.  Concept Lattice Generation by Singular Value Decomposition , 2004, CLA.

[27]  Jinkun Chen,et al.  Relations of reduction between covering generalized rough sets and concept lattices , 2015, Inf. Sci..

[28]  Hualong Yu,et al.  Rough set based semi-supervised feature selection via ensemble selector , 2019, Knowl. Based Syst..

[29]  Jiye Liang,et al.  A heuristic method to attribute reduction for concept lattice , 2010, 2010 International Conference on Machine Learning and Cybernetics.

[30]  Marianne Huchard,et al.  Performances of Galois Sub-hierarchy-building Algorithms , 2007, ICFCA.

[31]  L. Cser,et al.  The role of neural networks in the optimisation of rolling processes , 1998 .

[32]  Mei-Zheng Li,et al.  Attribute reduction in fuzzy decision formal contexts , 2011, 2011 International Conference on Machine Learning and Cybernetics.

[33]  Radim Belohlávek,et al.  Formal concept analysis over attributes with levels of granularity , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[34]  Michael D. Rice,et al.  Clusters, Concepts, and Pseudometrics , 2001, MFCSIT.

[35]  Bénédicte Le Grand,et al.  Conceptual and statistical footprints for social networks' characterization , 2009, SNA-KDD '09.

[36]  Vilém Vychodil,et al.  Formal Concept Analysis with Constraints by Closure Operators , 2006, ICCS.

[37]  Yong-Taek Im,et al.  Development of fuzzy control algorithm for shape control in cold rolling , 1995 .

[38]  Jian-Jun Qi,et al.  Attribute reduction in formal contexts based on a new discernibility matrix , 2009 .

[39]  Witold Pedrycz,et al.  A completeness analysis of frequent weighted concept lattices and their algebraic properties , 2012, Data Knowl. Eng..

[40]  Aleksey Buzmakov,et al.  Scalable Estimates of Concept Stability , 2014, ICFCA.

[41]  Luis E. Zárate,et al.  Extracting reducible knowledge from ANN with JBOS and FCANN approaches , 2013, Expert Syst. Appl..

[42]  Cherukuri Aswani Kumar,et al.  FUZZY CLUSTERING-BASED FORMAL CONCEPT ANALYSIS FOR ASSOCIATION RULES MINING , 2012, Appl. Artif. Intell..

[43]  Hua Mao Characterization and reduction of concept lattices through matroid theory , 2014, Inf. Sci..

[44]  Václav Snásel,et al.  On Concept Lattices and Implication Bases from Reduced Contexts , 2008, ICCS Supplement.

[45]  Itishree Mohanty,et al.  Artificial Neural Network and Its Application in Steel Industry , 2016 .

[46]  Luis E. Zárate,et al.  Representation and control of the cold rolling process through artificial neural networks via sensitivity factors , 2008 .

[47]  Duo Pei,et al.  Attribute reduction in decision formal context based on homomorphism , 2011, Int. J. Mach. Learn. Cybern..

[48]  Jinhai Li,et al.  A heuristic knowledge-reduction method for decision formal contexts , 2011, Comput. Math. Appl..

[49]  Jinhai Li,et al.  Knowledge reduction in real decision formal contexts , 2012, Inf. Sci..

[50]  Jesús Medina,et al.  Relating attribute reduction in formal, object-oriented and property-oriented concept lattices , 2012, Comput. Math. Appl..

[51]  Sergei O. Kuznetsov,et al.  On stability of a formal concept , 2007, Annals of Mathematics and Artificial Intelligence.

[52]  Michal Krupka,et al.  Subset-Generated Complete Sublattices as Concept Lattices , 2015, CLA.

[53]  Jirapond Tadrat,et al.  A new similarity measure in formal concept analysis for case-based reasoning , 2012, Expert Syst. Appl..

[54]  Sérgio M. Dias,et al.  Um Arcabouço para Desenvolvimento de Algoritmos da Análise Formal de Conceitos , 2011, RITA.

[55]  Theresa Beaubouef,et al.  Rough Sets , 2019, Lecture Notes in Computer Science.

[56]  Yves Bastide,et al.  Computing Proper Implications , 2001 .

[57]  Luis E. Zárate,et al.  FCANN: A new approach for extraction and representation of knowledge from ANN trained via Formal Concept Analysis , 2008, Neurocomputing.

[58]  Ivo Düntsch,et al.  Simplifying Contextual Structures , 2015, PReMI.

[59]  Xizhao Wang,et al.  Comparison of reduction in formal decision contexts , 2017, Int. J. Approx. Reason..

[60]  Wen-Xiu Zhang,et al.  Attribute reduction theory and approach to concept lattice , 2007, Science in China Series F: Information Sciences.

[61]  Gerd Stumme,et al.  Computing iceberg concept lattices with T , 2002, Data Knowl. Eng..

[62]  Hong Wang,et al.  Approaches to knowledge reduction in generalized consistent decision formal context , 2008, Math. Comput. Model..

[63]  Jian-guo Cao,et al.  Prediction Model of Rolling Force for Electrical Steel Based on Finite Element Method and Neural Network 1 , 2014 .

[64]  V. Snasel,et al.  Behavior of the Concept Lattice Reduction to visualizing data after Using Matrix Decompositions , 2007, 2007 Innovations in Information Technologies (IIT).

[65]  Douglas R. Vogel,et al.  Complexity Reduction in Lattice-Based Information Retrieval , 2005, Information Retrieval.

[66]  Samir Elloumi,et al.  Using minimal generators for composite isolated point extraction and conceptual binary relation coverage: Application for extracting relevant textual features , 2016, Inf. Sci..

[67]  Radim Belohlávek,et al.  Formal concept analysis with hierarchically ordered attributes , 2004, Int. J. Gen. Syst..

[68]  Sérgio M. Dias,et al.  Concept lattices reduction: Definition, analysis and classification , 2015, Expert Syst. Appl..

[69]  David Forge,et al.  Incremental Construction of Alpha Lattices and Association Rules , 2010, KES.

[70]  Ernestina Menasalvas Ruiz,et al.  A Model PM for Preprocessing and Data Mining Proper Process , 2007, Trans. Rough Sets.

[71]  Alain Gély Links between Modular Decomposition of Concept Lattice and Bimodular Decomposition of a Context , 2011, CLA.

[72]  J. M. Alexander On the theory of rolling , 1972, Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences.

[73]  Xia Wang,et al.  Attribute reduction in concept lattices based on deletion transformations , 2010, 2010 Sixth International Conference on Natural Computation.

[74]  Jinhai Li,et al.  Knowledge reduction in decision formal contexts , 2011, Knowl. Based Syst..

[75]  Radim Belohlávek,et al.  Selecting Important Concepts Using Weights , 2011, ICFCA.