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Eduardo Raul Hruschka, PhD.

Associate Professor

Computer Science Department (SCC/ICMC)

University of São Paulo (USP) at São Carlos

Av. Trabalhador São-carlense, 400, Centro

Office 4.126, CEP 13.566-590, São Carlos, SP, Brazil

Phone: +55 (16) 3373-6629

Home page: http://www.icmc.usp.br/~erh/index.htm

E-mail: erh at icmc usp br

 

 

 

 

I received my B.Sc. degree in Civil Engineering from Federal University of Paraná, Brazil, in 1995, and my M.Sc. and Ph.D. degrees in Computational Systems from COPPE/Federal University of Rio de Janeiro, Brazil, in 1998 and 2001, respectively. In 2006, I was awarded with a “Young Investigator Award”, which is given as a research grant by FAPESP (São Paulo Research Foundation). From 2010 to 2012, I worked as a visiting researcher at the University of Texas at Austin, USA, with Prof. Joydeep Ghosh´s research group. I am currently associate professor at the Computer Science Department (ICMC) of the University of São Paulo (USP) at São Carlos. I am also an Advanced Research Fellow of the Brazilian National Research Council (CNPq).  My main interests are data mining (a.k.a predictive analytics, data science, big data) and machine learning, with particular emphasis on classification and clustering methods, semi-supervised learning, classifier and cluster ensembles, knowledge transfer, transfer learning, evolutionary algorithms, data preparation (missing values imputation and feature selection), Bayesian methods, and neural networks.

 

Full CV (Portuguese): http://lattes.cnpq.br/8777859677671430

Google Scholar: http://scholar.google.com/citations?user=KGgOhbAAAAAJ&hl=en

 

 

Former Academic Positions

 

  • Assistant Professor, Graduate Program in Computer Science, University of Santos, Brazil (2003-2007).
  • Assistant Professor, Computer Engineering, Positivo University Center, Brazil (2002).
  • Assistant Professor, Computer Science, Tuiuti University of Paraná, Brazil (2002).

 

  

Service Activities

 

I am associate editor of Information Sciences, Elsevier.

 

I have joined the program committee of several conferences such as:

  • 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2011.
  • The IEEE International Conference on Data Mining (IEEE ICDM – 2006, 2007).
  • The 8th International Conference on Advanced Data Mining and Applications (ADMA 2012).
  • 6th International Conference on Hybrid Artificial Intelligence Systems (HAIS 2011).
  • International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2011).
  • The International Conference on Pattern Recognition Applications (ICPRAM 2012).
  • The International Conference on Intelligent Systems Design and Applications (ISDA - 2007, 2008, 2011).
  • The Twenty First Int. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA-AIE, 2008).
  • The International Conference on Intelligent Computing (ICIC 2007).

 

I have worked as reviewer for several international journals:

  • IEEE Transactions on Knowledge and Data Engineering.
  • IEEE Transactions on Systems, Man and Cybernetics – Parts A, B and C.
  • IEEE Transactions on Evolutionary Computation.
  • IEEE Transactions on Neural Networks.
  • IEEE Transactions on Fuzzy Systems.
  • Statistical Analysis and Data Mining.
  • ACM Journal of Data and Information Quality.
  • WIREs Data Mining and Knowledge Discovery.
  • Neurocomputing, Elsevier.
  • Pattern Analysis and Applications.
  • Information Sciences, Elsevier.
  • Pattern Recognition Letters, Elsevier.
  • Computational Statistics and Data Analysis, Elsevier.
  • Applied Soft Computing, Elsevier.
  • Journal of Heuristics, Springer.
  • Bioinformatics, Oxford University Press.
  • Soft Computing, Springer.
  • Journal of Experimental and Theoretical Artificial Intelligence, Taylor and Francis.
  • Machine Learning, Springer.

  

International Publications

 

Journal Papers

  • Covões, T. F., Hruschka, E. R., Ghosh, J. A Study of K-Means-based Algorithms for Constrained Clustering. Intelligent Data Analysis, IOS Press, to appear.
  • Silva, J. A., Hruschka, E. R. . An Experimental Study on the Use of Nearest-Neighbors Based Imputation Algorithms for Classification Tasks. Data & Knowledge Engineering, Elsevier, to appear, http://dx.doi.org/10.1016/j.datak.2012.12.006.
  • Covões, Thiago F., Hruschka, E.R., Ghosh, J. Competitive Learning with Pairwise Constraints. IEEE Transactions on Neural Networks and Learning Systems, v. 24, p. 164-169, 2013.
  • Nassif, L. F., Hruschka, E. R. . Document Clustering for Forensic Analysis: An Approach for Improving Computer Inspection. IEEE Transactions on Information Forensics and Security, v. 8, p. 46-54, 2013.
  • Coletta, L. F. S., Vendramin, L., Hruschka, E.R. ; Campello, R.J.G.B. ; Pedrycz, W. . Collaborative Fuzzy Clustering Algorithms: Some Refinements and Design Guidelines. IEEE Transactions on Fuzzy Systems, v. 20, p. 444-462, 2012.
  • Hruschka Jr., E. R., Hruschka, E.R., Ebecken, N.F.F., A Bayesian imputation method for a clustering genetic algorithm. Journal of Computational Methods in Sciences and Engineering, v. 11, p. 173-183, IOS Press, 2011.
  • Dos Santos, E. B., Hruschka Jr., E.R., Hruschka, E.R., Ebecken, N.F.F., Bayesian network classifiers: beyond classification accuracy. Intelligent Data Analysis, v. 15, p. 279-298, IOS Press, 2011.
  • Naldi, M.C., Campello, R.J.G.B., Hruschka, E.R., Carvalho, A.C.P.L.F., Efficiency issues of evolutionary k-means. Applied Soft Computing, v. 11, p. 1938-1952, 2011.
  • Kanda, J., Hruschka, E.R., de Carvalho, A. C. P. L. F., Soares, C. . Selection of algorithms to solve traveling salesman problems using meta-learning. International Journal of Hybrid Intelligent Systems, v. 8, p. 117-128, 2011.
  • Covões, T. F., Hruschka, E. R., Towards improving cluster-based feature selection with a simplified silhouette filter. Information Sciences, p. 3766-3782, 2011.
  • Vendramin, L., Campello, R.J.G.B., Hruschka, E.R. Relative clustering validity criteria: A comparative overview. Statistical Analysis and Data Mining, v. 3, p. 209-235, 2010.
  • Hruschka, E. R., Campello, R. J. G. B., Freitas, A. A., de Carvalho, A. C. P. L. F., A Survey of Evolutionary Algorithms for Clustering. IEEE Transactions on Systems, Man and Cybernetics - Part C: Applications and Reviews, v. 39, p. 133-155, 2009.
  • Hruschka, E.R., Garcia, A.J.T., Hruschka Jr., E.R., Ebecken, N.F.F., On the Influence of Imputation in Classification: Practical Issues. Journal of Experimental and Theoretical Artificial Intelligence, v. 21, p. 43-58, Taylor & Francis, 2009.
  • Campello, R. J. G. B., Hruschka, E. R., On Comparing Two Sequences of Numbers and Its Applications to Clustering Analysis, Information Sciences, v. 179, p. 1025-1039, Elsevier, 2009.
  • Campello, R.J.G.B., Hruschka, E.R., Alves, V.S., On the Efficiency of Evolutionary Fuzzy Clustering, Journal of Heuristics, v. 15, p. 43-75, Springer, 2009.
  • Hruschka Jr., E.R., Hruschka, E.R., Ebecken, N.F.F., Bayesian Networks for Imputation in Classification Problems, Journal of Intelligent Information Systems, Springer, v. 29, p. 231-252, Springer, 2007.
  • Senger, H., Hruschka, E.R., Silva, F.A.B., Sato, L.M., Bianchini, C.P., Jerocsh, B.F., Exploiting Idle Cycles to Execute Data Mining Applications on Clusters of PCs, Journal of Systems and Software, v. 80, 778-790, Elsevier, 2007.
  • Campello, R.J.G.B., Hruschka, E.R. A fuzzy extension of the silhouette width criterion for cluster analysis, Fuzzy Sets and Systems, v. 157, pp. 2858-2875, Elsevier, 2006.
  • Hruschka, E.R., Ebecken, N.F.F., Extracting Rules from Multilayer Perceptrons in Classification Problems: A Clustering-based Approach, Neurocomputing, Elsevier, v. 70, 384-397, 2006.
  • Hruschka, E.R., Campello, R.J.G.B., de Castro, L.N., Evolving Clusters in Gene-Expression Data, Information Sciences, 176 (13), 1898-1927, Elsevier, 2006.
  • Hruschka, E. R., Hruschka A Jr.., E.R., Covões, T.F., Ebecken, N.F.F., Bayesian Feature Selection for Clustering Problems. Journal of Information & Knowledge Management, v. 5, 315-327, World Scientific, 2006.
  • Vizine, A., Castro, L.N., Hruschka, E.R., Gudwin, R., Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm. Informatica - An International Journal of Computing and Informatics, Slovenian Society Informatika, v. 29, n. 2, 143-154, 2005. 
  • Hruschka, E. R.; Ebecken, N.F.F., A genetic algorithm for cluster analysis. Intelligent Data Analysis (IDA), IOS Press, v. 7, n. 1, 15-25, 2003. 
  • Hruschka JR., E.R.; Hruschka, E.R.; Ebecken, N.F.F., A Feature Selection Bayesian Approach for Extracting Classification Rules with a Clustering Genetic Algorithm. Applied Artificial Intelligence, Taylor & Francis, London, v. 17, n. 5-6, 489-506, 2003. 
  • Hruschka, E.R.; Ebecken, N.F.F., A Clustering Genetic Algorithm for Extracting Rules from Supervised Neural network Models in Data Mining Tasks. International Journal of Computers, Systems and Signals, Special Issue: Knowledge Discovery from Structured and Unstructured Data, IAAMSAD, v. 1, 17-29, 2000. 

 

Book Chapters

  • Horta, D., Naldi, M. C., Campello, Ricardo J. G. B., Hruschka, E. R., de Carvalho, A. C. P. L. F. Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues, In: Ajith Abraham; Aboul-Ella Hassanien; Andre de Carvalho; Vaclav Snasel. (Org.). Foundations of Computational Intelligence (Bio-Inspired Data Mining). Berlin: Springer, v. 4, p. 167-195.
  • Naldi, M. C., A. C. P. L. F. Carvalho, R. J. G. B. Campello, E. R. Hruschka. Genetic Clustering for Data Mining, In Soft Computing for Knowledge Discovery and Data Mining, edited by Oded Maimon; Lior Rokach. v. 1, p. 113-132, Springer, 2008.
  • Hruschka, E. R., L. N. C. Silva, R. J. G. B. Campello. Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between k-Means and Evolutionary Search In Studies in Computational Intelligence - Hybrid Evolutionary Algorithms, edited by Crina Grosan; Ajith Abraham; Hisao Ishibuchi. Vol. 75, 313-335. Springer Berlin/Heidelberg, 2007.
  • Sherafat, V., L. N. Castro, E. R. Hruschka. The Influence of Pheromone and Adaptive Vision in the Standard Ant Clustering Algorithm In Recent Developments in Biologically Inspired Computing, edited by Leandro Nunes de Castro; Fernando J. Von Zuben. Vol. 1, 207-234. Hershey, PA: Idea Group Publishing, 2004.

 

Conference Proceedings

  • Acharya, A. ; Hruschka, E.R. ; Ghosh, J. ; Acharyya, S. . Transfer Learning with Cluster Ensembles. In: ICML 2011 Workshop on Unsupervised and Transfer Learning, 2012, Bellevue, Washington, USA. JMLR Workshop and Conference Proceedings: Unsupervised and Transfer Learning workshop. Boston : JMLR, 2011. v. 27. p. 123-132.
  • Marcacini, R. M., Hruschka, E.R., Rezende, S. O. On the Use of Consensus Clustering for Incremental Learning of Topic Hierarchies. In: 21th Brazilian Symposium on Artificial Intelligence (SBIA 2012), 2012, Curitiba. Lecture Notes in Computer Science (LNCS). Berlin: Springer, 2012. v. 7589. p. 112-121.
  • Kanda, J., Soares, C., Hruschka, E. R., de Carvalho, A. C. P. L. F. A Meta-Learning Approach to Select Meta-Heuristics for the Traveling Salesman Problem Using MLP-Based Label Ranking. In: 19th International Conference on Neural Information Processing, 2012, Doha, Qatar. Lecture Notes in Computer Science (LNCS). Berlin: Springer, 2012. v. 7665. p. 488-495.
  • Sestaro, D. M., Covões, T. F., Hruschka, E.R. A Semi-Supervised Approach to Estimate the Number of Clusters per Class. In: 2012 Brazilian Symposium on Neural Networks (SBRN 2012), 2012, Curitiba. Proceedings of the 2012 Brazilian Symposium on Neural Networks. Piscataway: IEEE Press, 2012. v. 1. p. 73-78.
  • Vendramin, Lucas ; Campello, R.J.G.B. ; Coletta, L. F. S. ; Hruschka, E.R. . Distributed Fuzzy Clustering with Automatic Detection of the Number of Clusters. In: International Symposium on Distributed Computing and Artificial Intelligence (DCAI'11), 2011, Salamanca. Advances in Intelligent and Soft Computing. Berlin : Springer, 2011. v. AISC91. p. 133-140.
  • Acharya, A. ; Hruschka, E.R. ; Ghosh, J. ; Acharyya, S. . C3E: A Framework for Combining Ensembles of Classifiers and Clusterers. In: 10th International Workshop on Multiple Classifier Systems (MCS'11), 2011, Naples, Italy. Lecture Notes in Computer Science. Heidelberg : Springer, 2011. v. 6713. p. 269-278.
  • Acharya, A. ; Hruschka, E. R. ; Ghosh, J. . A Privacy-Aware Bayesian Approach for Combining Classifier and Cluster Ensembles. In: The Third IEEE International Conference on Information Privacy, Security, Risk and Trust (PASSAT 2011), 2011, MIT, Boston. Proceedings of the Third IEEE International Conference on Information Privacy, Security, Risk and Trust. Washington, DC : IEEE Computer Press, 2011. p. 1169-1172.
  • Kanda, J. ; Carvalho, André C.P.L.F. ; Hruschka, E.R. ; Soares, C. . Using meta-learning to recommend meta-heuristics for the traveling salesman problem. In: The Tenth International Conference on Machine Learning and Applications (ICMLA'11), 2011, Honolulu. Proceedings of the The Tenth International Conference on Machine Learning and Applications. Washington, DC : IEEE Press, 2011. v. 1. p. 346-351.
  • Covões, Thiago F. ; Hruschka, E.R. . Splitting and Merging Gaussian Mixture Model Components: An Evolutionary Approach. In: The Tenth International Conference on Machine Learning and Applications (ICMLA'11), 2011, Honolulu. Proceedings of the The Tenth International Conference on Machine Learning and Applications (ICMLA'11). Washington, DC : IEEE Press, 2011. v. 1. p. 106-111.
  • Nassif, L. F. ; HRUSCHKA, E. R. . Document Clustering for Forensic Computing: An Approach for Improving Computer Inspection. In: The Tenth International Conference on Machine Learning and Applications (ICMLA'11), 2011, Honolulu. Proceedings of the International Conference on Machine Learning and Applications (ICMLA'11). Washington, DC : IEEE Press, 2011. v. 1. p. 265-268.
  • Silva, J. A. ; HRUSCHKA, E. R. . Extending k-Means-Based Algorithms for Evolving Data Streams with Variable Number of Clusters. In: The Tenth International Conference on Machine Learning and Applications (ICMLA'11), 2011, Honolulu. Proceedings of the International Conference on Machine Learning and Applications (ICMLA'11). Washington, DC : IEEE Press, 2011. v. 2. p. 14-19.
  • Coletta, L. F. S. ; Hruschka, E. R. ; Covoes, T. F., Campello, R.J.G.B. . Fuzzy Clustering-Based Filter. In: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2010), Dortmund. Communications in Computer and Information Science. Berlin : Springer, 2010. v. 80. p. 406-415. 
  • Dos Santos, E. B, Hruschka Jr., E.R., Hruschka, E. R., Ebecken, N. F. F. A Distance-Based Mutation Operator for Learning Bayesian Network Structures using Evolutionary Algorithms (accepted). In: 2010 IEEE Congress on Evolutionary Computation (WCCI'10), Barcelona. 
  • Kanda, J. ; de Carvalho, A. C. P. L. F. ; Hruschka, E. R. ; Soares, C. . Using Meta-learning to Classify Traveling Salesman Problems (accepted). In: Brazilian Symposium on Artificial Neural Networks (SBRN 2010), São Bernardo do Campo, SP. 
  • Jaskowiak, P. A. ; Campello, R.J.G.B. ; Covoes, T. F., Hruschka, E. R. . A Comparative Study on the Use of Correlation Coefficients for Redundant Feature Elimination (accepted). In: Brazilian Symposium on Artificial Neural Networks, 2010, São Bernardo do Campo, 2010. 
  • Vendramin, L., Campello, R. J. G. B., Hruschka, E. R. On the Comparison of Relative Clustering Validity Criteria, In: 2009 SIAM International Conference on Data Mining (SDM 09), Sparks, Nevada, v. 1. p. 733-744, 2009.
  • Covões, T. F., Hruschka, E.R., Castro, L.N., dos Santos, A. M. A Cluster-Based Feature Selection Approach. In: The 4th International Conference on Hybrid Artificial Intelligence Systems (HAIS'09), 2009, Salamanca, Espanha. Lecture Notes in Artificial Intelligence (LNAI 5572), Berlin, Springer, 2009, p. 169-176. 
  • Silva, J. A., Hruschka, E. R. An Evolutionary Algorithm for Missing Values Substitution in Classification Tasks. In: The 4th International Conference on Hybrid Artificial Intelligence Systems (HAIS'09), 2009, Salamanca, Espanha. Lecture Notes in Artificial Intelligence (LNAI 5572), Berlin, Springer, 2009, p. 195-202. 
  • Vendramin, L., Campello, R.J.G.B., Hruschka, E. R. A Robust Methodology for Comparing Performances of Clustering Validity Criteria. In: 19th Brazilian Symposium on Artificial Intelligence (SBIA), 2008, Salvador. Lecture Notes in Artificial Intelligence (LNAI 5249), p. 237-247.
  • Campello, R. J. G. B., Hruschka, E. R. A Fully Sensitive Correlation Measure for Data Mining. In: Ninth International Conference on Data Mining, Protection, Detection and other Security Technologies, 2008, Cadiz. WIT Transactions on Information and Communications Technologies. Ashurst Lodge, Southampton, WIT Press, 2008. v. 40. p. 35-41.
  • Alves, V. S., R. J. G. B. Campello, E. R. Hruschka. 2007. A Fuzzy Variant of an Evolutionary Algorithm for Clustering In Proceedings of the 2007 IEEE International Conference on Fuzzy Systems, London 2007 1 375-380, IEEE Press.
  • Hruschka, E. R., T. F. Covões, E. R. Hruschka Jr. et al.. 2007. Adapting Supervised Feature Selection Methods for Clustering Tasks In Managing Worldwide Operations and Communications with Information Technology (IRMA 2007 Proceedings) 2007 Information Resources Management Association (IRMA) International Conference Vancouver 2007 99-102 Hershey: Idea Group Publishing.
  • Pedro, S. D. S., E. R. Hruschka Jr., E. R. Hruschka, N. F. F. Ebecken et al.. 2007. WNB: A Weighted Naïve Bayesian Classifier, to appear, In Proceedings of the International Conference on Intelligent Systems Design and Applications (ISDA´07) Rio de Janeiro 2007, p. 138-142, IEEE Press.
  • Alves, V. S., R. J. G. B. Campello, E. R. Hruschka. 2006. Towards a Fast Evolutionary Algorithm for Clustering In Proceedings of the 2006 IEEE Congress on Evolutionary Computation (CEC 2006), Vancouver 2006 6240-6247, IEEE Press.
  • Hruschka Jr., E. R., E. R. Hruschka, N. F. F. Ebecken. 2005. Applying Bayesian Networks for Meteorological Data Mining In Applications and Innovations in Intelligent Systems XIII The 25th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence Cambridge 2005 122-133 Berlin: Springer-Verlag.
  • Hruschka, E. R., T. F. Covões. 2005. Feature Selection for Cluster Analysis: an Approach Based on the Simplified Silhouette Criterion In Proceedings of the International Conference on Computational Intelligence for Modeling Control and Automation - CIMCA'2005, Vienna 2005 I 32-38 Piscataway, New Jersey: IEEE Computer Society Press.
  • Hruschka, E. R., E. R. Hruschka Jr., T. F. Covões et al.. 2005. Feature Selection for Clustering Problems: a Hybrid Algorithm that Iterates Between k-means and a Bayesian Filter In Proceedings of The Fifth International conference on Hybrid Intelligent Systems (HIS'05), Rio de Janeiro 2005 1 405-410 Los Alamitos, CA, USA: IEEE Computer Society.
  • Campello, R. J. G. B., E. R. Hruschka. 2005. Fuzzy Silhouette: An Alternative Cluster Validity Measure In Proceedings of the Eleventh International Fuzzy Systems Association World Congress (IFSA 2005) Beijing 2005 I 603-608 Beijing: Tsinghua University Press & Springer.
  • Hruschka, E. R., E. R. Hruschka Jr., N. F. F. Ebecken. 2005. Missing Values Imputation for a Clustering Genetic Algorithm In Lecture Notes in Computer Science 3612 (Advances in Natural Computation) First International Conference on Natural Computation (ICNC´05) Changsha 2005 3612 245-254 Berlin: Springer-Verlag.
  • Garcia, A. J. T., E. R. Hruschka. 2005. Naïve Bayes as an Imputation Tool for Classification Problems In Proceedings of the Fifth International conference on Hybrid Intelligent Systems (HIS'05), Rio de Janeiro 2005 1 497-499 Los Alamitos, CA, USA: IEEE Computer Society.
  • Silva, F. A. B., S. Carvalho, E. R. Hruschka. 2004. A Scheduling Algorithm for Running Bag-of-Tasks Data Mining Applications on the Grid In Lecture Notes in Computer Science (LNCS) 10th International Euro-Par Conference 2004 Pisa 2004 3149 254-262 Berlin: Springer-Verlag.
  • Silva, L. N. C., E. R. Hruschka, R. J. G. B. Campello. 2004. An Evolutionary Clustering Technique with Local Search to Design RBF Neural Network Classifiers In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2004) Budapest 2004 2083-2088 Piscataway, NJ: IEEE Press.
  • Hruschka, E. R., L. N. Castro, R. J. G. B. Campello. 2004, Evolutionary Algorithms for Clustering Gene-Expression Data In Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM´04), Brighton, UK 2004 403-406 Piscataway, NJ: IEEE Press.
  • Hruschka, E. R., R. J. G. B. Campello, L. N. C. Silva. 2004. Evolutionary Search for Optimal Fuzzy C-Means Clustering In Proceedings of the 2004 IEEE International Conference on Fuzzy Systems,  Budapest 2004 2 685-690 Piscataway, NJ: IEEE Press.
  • Hruschka Jr., E. R., E. R. Hruschka, N. F. F. Ebecken. 2004. Feature Selection by Bayesian Networks In Lecture Notes in Computer Science 3060 - Advances in Artificial Intelligence The Seventeenth Canadian Conference on Artificial Intelligence (AI'04) London, Ontario 2004 3060 370-379 Berlin: Springer.
  • Hruschka, E. R., R. J. G. B. Campello, L. N. C. Silva. 2004. Improving the Efficiency of a Clustering Genetic Algorithm In Lecture Notes in Computer Science (LNCS 3315) 9th Ibero-American Conference on Artificial Intelligence (IBERAMIA 2004) Puebla 2004 3315 861-870 Berlin: Springer-Verlag.
  • Senger, H., E. R. Hruschka, F. A. B. Silva et al.. 2004. Inhambu: Data Mining Using Idle Cycles in Clusters of PCs In Lecture Notes in Computer Science (LNCS) 3222 IFIP International Conference on Network and Parallel Computing (NPC 2004) Wuhan 2004 3222 213-220 Berlin: Springer-Verlag.
  • Silva, F. A. B., S. Carvalho, H. Senger et al.. 2004. Running Data Mining Applications on the Grid: a Bag-of-Tasks Approach In Lecture Notes in Computer Science (LNCS 3044) The 2004 International Conference on Computational Science and its Applications (ICCSA 2004) Assisi (Perugia, Italy) 2004 3044 168-177 Berlin: Springer-Verlag.
  • Sherafat, V., L. N. C. Silva, E. R. Hruschka. 2004. TermitAnt: An Ant Clustering Algorithm Improved by Ideas from Termite Colonies In Lecture Notes in Computer Science (LNCS 3316) 11th International Conference on Neural Information Processing (ICONIP 2004) Calcutá 2004 3316 1088-1093 Berlin: Springer-Verlag.
  • Hruschka, E. R., E. R. Hruschka Jr., N. F. F. Ebecken. 2004. Towards Efficient Imputation by Nearest-Neighbors: A Clustering Based Approach In Lecture Notes in Artificial Intelligence 3339 17th Australian Joint Conference on Artificial Intelligence (AI´04) Cairns 2004 3339 513-525 Berlin: Springer.
  • Hruschka Jr., E. R., E. R. Hruschka, N. F. F. Ebecken. 2003. A feature selection Bayesian approach for a clustering genetic algorithm In Fourth International Conference on Data Mining (Data Mining 2003) Rio de Janeiro 2003 1 181-192 Southampton, UK.: WIT Press.
  • Hruschka, E. R., E. R. Hruschka Jr., N. F. F. Ebecken. 2003. A Nearest-Neighbor Method as a Data Preparation Tool for a Clustering Genetic Algorithm In Proceedings of the 18th Brazilian Symposium on Databases / ACM SIGMOD Disk (SBBD 2003) Manaus 2003 1 319-327 Manaus: Editora da Universidade Federal do Amazonas.
  • Hruschka, E. R., E. R. Hruschka Jr., N. F. F. Ebecken. 2003. Evaluating a Nearest-Neighbor Method to Substitute Continuous Missing Values In Lecture Notes in Artificial Intelligence (LNAI 2903) The 16th Australian Conference on Artificial Intelligence - AI´03 Perth 2003 2903 723-734 Berlin: Springer.
  • Hruschka, E. R., N. F. F. Ebecken. 2002. A clustering genetic algorithm for extracting rules from multilayer perceptrons trained in classification problems In Third International Conference on Data Mining Bologna 2002 1 451-460 Southampton, UK.: WIT Press.
  • Hruschka Jr., E. R., E. R. Hruschka, N. F. F. Ebecken. 2002. A Data Preparation Bayesian Approach for a Clustering Genetic Algorithm In Frontiers in Artificial Intelligence and Applications - Soft Computing Systems: Design, Management and Applications. The Second International Conference on Hybrid and Intelligent Systems (HIS 2002) Santiago 2002 87 453-461 Amsterdam, Holanda.: IOS Press.
  • Hruschka Jr., E. R., E. R. Hruschka, N. F. F. Ebecken. 2002. A Feature Selection Bayesian Approach for Extracting Classification Rules with a Clustering Genetic Algorithm In Proceedings of the First Workshop on Data Cleaning and Preprocessing. IEEE International Conference on Data Mining 2002, Maebashi TERRSA, Maebashi City 2002, IEEE Press.
  • Ebecken, N. F. F., E. R. Hruschka. 2001. Rules from Supervised Neural Networks in Data Mining Tasks In Frontiers in Artificial Intelligence and Applications Second Congress of Logic Applied to Technology São Paulo 2001 71 84-100 Amsterdam, Holanda: IOS Press.
  • Hruschka, E. R., N. F. F. Ebecken. 2000. Applying a Clustering Genetic Algorithm for Extracting Rules from a Supervised Neural Network In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 2000) Como, Italy 2000 Piscataway, N.J., USA: IEEE Press.
  • Hruschka, E. R., N. F. F. Ebecken. 2000. Credit approval by a clustering genetic algorithm In Second International Conference on Data Mining Cambridge, UK. 2000 1 403-412 Southampton, UK.: WIT Press.
  • Hruschka, E. R., N. F. F. Ebecken. 2000. Using a Clustering Genetic Algorithm for Rule Extraction from Artificial Neural Networks In Proceedings of the First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, San Antonio, Texas, USA 2000 199-206 Piscataway, NJ, USA: IEEE Press.
  • Hruschka, E. R., N. F. F. Ebecken. 1999. Rule Extraction from Neural Networks: Modified RX Algorithm In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN 99) Washington, DC, USA 1999 Piscataway, NJ, USA: IEEE Press.
  • Hruschka, E. R., N. F. F. Ebecken. 1998. Rule Extraction from Neural Networks in Data Mining Applications In International Conference on Data Mining Rio de Janeiro 1998 1 289-301 Southampton, UK.: WIT Press.

 

Updated: January, 2013.