Eduardo R. Hruschka

发表

Running Data Mining Applications on the Grid: A Bag-of-Tasks Approach

Hermes Senger, Fabrício Alves Barbosa da Silva, Sílvia Carvalho, 2004, ICCSA.

Relative clustering validity criteria: A comparative overview

Ricardo J. G. B. Campello, Eduardo R. Hruschka, Lucas Vendramin, 2010, Stat. Anal. Data Min..

A Scheduling Algorithm for Running Bag-of-Tasks Data Mining Applications on the Grid

Fabrício Alves Barbosa da Silva, Sílvia Carvalho, Eduardo R. Hruschka, 2004, Euro-Par.

Transfer Learning with Cluster Ensembles

Joydeep Ghosh, Ayan Acharya, Eduardo R. Hruschka, 2011, ICML Unsupervised and Transfer Learning.

Distributed Fuzzy Clustering with Automatic Detection of the Number of Clusters

Ricardo J. G. B. Campello, Eduardo R. Hruschka, Lucas Vendramin, 2011, DCAI.

Towards a Fast Evolutionary Algorithm for Clustering

Ricardo J. G. B. Campello, Eduardo R. Hruschka, Vinicius S. Alves, 2006, 2006 IEEE International Conference on Evolutionary Computation.

Towards Efficient Imputation by Nearest-Neighbors: A Clustering-Based Approach

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2004, Australian Conference on Artificial Intelligence.

A Bayesian imputation method for a clustering genetic algorithm

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2011, J. Comput. Methods Sci. Eng..

A Survey and Comparative Study of Tweet Sentiment Analysis via Semi-Supervised Learning

Eduardo R. Hruschka, Luiz F. S. Coletta, Nádia Félix Felipe da Silva, 2016, ACM Comput. Surv..

Combining clustering and active learning for the detection and learning of new image classes

Joydeep Ghosh, Ayan Acharya, Eduardo R. Hruschka, 2019, Neurocomputing.

Evolutionary search for optimal fuzzy c-means clustering

Ricardo J. G. B. Campello, Eduardo R. Hruschka, L. N. de Castro, 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

Evolving clusters in gene-expression data

Ricardo J. G. B. Campello, Leandro Nunes de Castro, Eduardo R. Hruschka, 2006, Inf. Sci..

Competitive Learning With Pairwise Constraints

Joydeep Ghosh, Eduardo R. Hruschka, Thiago F. Covoes, 2013, IEEE Transactions on Neural Networks and Learning Systems.

Genetic Clustering for Data Mining

André Carlos Ponce de Leon Ferreira de Carvalho, Ricardo J. G. B. Campello, Murilo Coelho Naldi, 2008, Soft Computing for Knowledge Discovery and Data Mining.

A Meta-Learning Approach to Select Meta-Heuristics for the Traveling Salesman Problem Using MLP-Based Label Ranking

André Carlos Ponce de Leon Ferreira de Carvalho, Carlos Soares, Eduardo R. Hruschka, 2012, ICONIP.

Interactive textual feature selection for consensus clustering

Ricardo M. Marcacini, Solange Oliveira Rezende, Eduardo R. Hruschka, 2015, Pattern Recognit. Lett..

Naive Bayes as an imputation tool for classification problems

Eduardo R. Hruschka, Antonio J. T. Garcia, Antonio J. T. Garcia, 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

Missing Values Imputation for a Clustering Genetic Algorithm

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2005, ICNC.

Extracting rules from multilayer perceptrons in classification problems: A clustering-based approach

Nelson F. F. Ebecken, Eduardo R. Hruschka, N. Ebecken, 2006, Neurocomputing.

Using Meta-learning to Recommend Meta-heuristics for the Traveling Salesman Problem

André Carlos Ponce de Leon Ferreira de Carvalho, Carlos Soares, Eduardo R. Hruschka, 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

Extending k-Means-Based Algorithms for Evolving Data Streams with Variable Number of Clusters

Eduardo R. Hruschka, Jonathan de Andrade Silva, J. Silva, 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

Time Series Decomposition Using Spring System Applied on Phase Spaces

Rodrigo Fernandes de Mello, Eduardo R. Hruschka, Ricardo Araújo Rios, 2018, 2018 7th Brazilian Conference on Intelligent Systems (BRACIS).

A Survey of Evolutionary Algorithms for Clustering

Alex Alves Freitas, André Carlos Ponce de Leon Ferreira de Carvalho, Ricardo J. G. B. Campello, 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

On comparing two sequences of numbers and its applications to clustering analysis

Ricardo J. G. B. Campello, Eduardo R. Hruschka, E. Hruschka, 2009, Inf. Sci..

Decomposing time series into deterministic and stochastic influences: A survey

Rodrigo Fernandes de Mello, Eduardo R. Hruschka, Ricardo Araújo Rios, 2019, Digit. Signal Process..

Rule Extraction From Neural Networks In DataMining Applications

Eduardo R. Hruschka, 1970 .

Splitting and Merging Gaussian Mixture Model Components: An Evolutionary Approach

Eduardo R. Hruschka, Thiago F. Covoes, T. Covões, 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

A fuzzy extension of the silhouette width criterion for cluster analysis

Ricardo J. G. B. Campello, Eduardo R. Hruschka, E. Hruschka, 2006, Fuzzy Sets Syst..

A genetic algorithm for cluster analysis

Nelson F. F. Ebecken, Eduardo R. Hruschka, N. Ebecken, 2003, Intell. Data Anal..

A Robust Methodology for Comparing Performances of Clustering Validity Criteria

Ricardo J. G. B. Campello, Eduardo R. Hruschka, Lucas Vendramin, 2008, SBIA.

Simultaneous co-clustering and learning to address the cold start problem in recommender systems

Eduardo R. Hruschka, Andre Luiz Vizine Pereira, André Luiz Vizine Pereira, 2015, Knowl. Based Syst..

Collaborative Fuzzy Clustering Algorithms: Some Refinements and Design Guidelines

Witold Pedrycz, Ricardo J. G. B. Campello, Eduardo R. Hruschka, 2012, IEEE Transactions on Fuzzy Systems.

Data stream clustering: A survey

André Carlos Ponce de Leon Ferreira de Carvalho, Elaine Ribeiro de Faria, João Gama, 2013, CSUR.

C 3E: A Framework for Combining Ensembles of Classifiers and Clusterers

Joydeep Ghosh, Ayan Acharya, Eduardo R. Hruschka, 2011, MCS.

Bayesian network classifiers: Beyond classification accuracy

Estevam R. Hruschka, Nelson F. F. Ebecken, Edimilson Batista dos Santos, 2011, Intell. Data Anal..

Selection of algorithms to solve traveling salesman problems using meta-learning

André Carlos Ponce de Leon Ferreira de Carvalho, Carlos Soares, Eduardo R. Hruschka, 2011, Int. J. Hybrid Intell. Syst..

An Optimization Framework for Semi-Supervised and Transfer Learning using Multiple Classifiers and Clusterers pdf

Joydeep Ghosh, Ayan Acharya, Eduardo R. Hruschka, 2012, ArXiv.

Unsupervised learning of Gaussian Mixture Models: Evolutionary Create and Eliminate for Expectation Maximization algorithm

Eduardo R. Hruschka, Thiago F. Covoes, E. Hruschka, 2013, 2013 IEEE Congress on Evolutionary Computation.

Rule extraction from neural networks: modified RX algorithm

Nelson F. F. Ebecken, Eduardo R. Hruschka, N. Ebecken, 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

A Nearest-Neighbor Method as a Data Preparation Tool for a Clustering Genetic Algorithm

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2003, SBBD.

Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm

Leandro Nunes de Castro, Ricardo R. Gudwin, Eduardo R. Hruschka, 2005, Informatica.

A Privacy-Aware Bayesian Approach for Combining Classifier and Cluster Ensembles

Joydeep Ghosh, Ayan Acharya, Eduardo R. Hruschka, 2011, 2011 IEEE Third Int'l Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third Int'l Conference on Social Computing.

Combining Classification and Clustering for Tweet Sentiment Analysis

Estevam R. Hruschka, Eduardo R. Hruschka, Nádia Félix F. da Silva, 2014, 2014 Brazilian Conference on Intelligent Systems.

Evolving Gaussian Mixture Models with Splitting and Merging Mutation Operators

Joydeep Ghosh, Eduardo R. Hruschka, Thiago F. Covoes, 2016, Evolutionary Computation.

An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks

Eduardo R. Hruschka, Jonathan de Andrade Silva, J. Silva, 2013, Data Knowl. Eng..

A Fuzzy Variant of an Evolutionary Algorithm for Clustering

Ricardo J. G. B. Campello, Eduardo R. Hruschka, Vinicius S. Alves, 2007, 2007 IEEE International Fuzzy Systems Conference.

A Semi-supervised Approach to Estimate the Number of Clusters per Class

Eduardo R. Hruschka, Thiago F. Covoes, Davidson M. Sestaro, 2012, 2012 Brazilian Symposium on Neural Networks.

Online Orthogonal Regression Based on a Regularized Squared Loss

Wagner Meira, Saul de Castro Leite, Eduardo R. Hruschka, 2018, 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA).

An Optimization Framework for Combining Ensembles of Classifiers and Clusterers with Applications to Nontransductive Semisupervised Learning and Transfer Learning

Joydeep Ghosh, Ayan Acharya, Eduardo R. Hruschka, 2014, TKDD.

A differential evolution algorithm to optimise the combination of classifier and cluster ensembles

Joydeep Ghosh, Ayan Acharya, Eduardo R. Hruschka, 2015, Int. J. Bio Inspired Comput..

A feature selection bayesian approach for extracting classification rules with a clustering genetic algorithm

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2003, Appl. Artif. Intell..

On the Use of Consensus Clustering for Incremental Learning of Topic Hierarchies

Ricardo M. Marcacini, Solange Oliveira Rezende, Eduardo R. Hruschka, 2012, SBIA.

Tweet sentiment analysis with classifier ensembles

Estevam R. Hruschka, Eduardo R. Hruschka, Nádia Félix F. da Silva, 2014, Decis. Support Syst..

Document Clustering for Forensic Analysis: An Approach for Improving Computer Inspection

Eduardo R. Hruschka, Luís Filipe da Cruz Nassif, Luís Filipe da Cruz Nassif, 2013, IEEE Transactions on Information Forensics and Security.

A Support System for Clustering Data Streams with a Variable Number of Clusters

Eduardo R. Hruschka, Jonathan de Andrade Silva, J. Silva, 2016, ACM Trans. Auton. Adapt. Syst..

An Experimental Study on Unsupervised Clustering-Based Feature Selection Methods

Eduardo R. Hruschka, Thiago F. Covoes, 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

A Cluster-Based Feature Selection Approach

Leandro Nunes de Castro, Eduardo R. Hruschka, Thiago F. Covoes, 2009, HAIS.

Evolutionary algorithms for clustering gene-expression data

Ricardo J. G. B. Campello, Leandro Nunes de Castro, Eduardo R. Hruschka, 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

Feature Selection for Cluster Analysis: an Approach Based on the Simplified Silhouette Criterion

Eduardo R. Hruschka, Thiago F. Covoes, E. Hruschka, 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).

Meta-learning to select the best meta-heuristic for the Traveling Salesman Problem: A comparison of meta-features

André Carlos Ponce de Leon Ferreira de Carvalho, Carlos Soares, Pavel Brazdil, 2016, Neurocomputing.

A Comparative Study on the Use of Correlation Coefficients for Redundant Feature Elimination

Ricardo J. G. B. Campello, Eduardo R. Hruschka, Pablo A. Jaskowiak, 2010, 2010 Eleventh Brazilian Symposium on Neural Networks.

An Evolutionary Algorithm for Missing Values Substitution in Classification Tasks

Eduardo R. Hruschka, Jonathan de Andrade Silva, J. Silva, 2009, HAIS.

EACImpute: An Evolutionary Algorithm for Clustering-Based Imputation

Eduardo R. Hruschka, Jonathan de Andrade Silva, J. Silva, 2009, 2009 Ninth International Conference on Intelligent Systems Design and Applications.

A study of K-Means-based algorithms for constrained clustering

Joydeep Ghosh, Eduardo R. Hruschka, Thiago F. Covoes, 2013, Intell. Data Anal..

Inhambu: Data Mining Using Idle Cycles in Clusters of PCs

Liria Matsumoto Sato, Hermes Senger, Fabrício Alves Barbosa da Silva, 2004, NPC.

The Influence of Pheromone and Adaptive Vision in the Standard Ant Clustering Algorithm

Leandro Nunes de Castro, Eduardo R. Hruschka, Vahid Sherafat, 2005 .

Exploiting idle cycles to execute data mining applications on clusters of PCs

Liria Matsumoto Sato, Hermes Senger, Fabrício Alves Barbosa da Silva, 2007, J. Syst. Softw..

Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble

Estevam R. Hruschka, Eduardo R. Hruschka, Nádia Félix F. da Silva, 2014, SemEval@COLING.

Bayesian networks for imputation in classification problems

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2007, Journal of Intelligent Information Systems.

Feature Selection by Bayesian Networks

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2004, Canadian Conference on AI.

On the Comparison of Relative Clustering Validity Criteria

Ricardo J. G. B. Campello, Eduardo R. Hruschka, Lucas Vendramin, 2009, SDM.

A Data Preparation Bayesian Approach for a Clustering Genetic Algorithm

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2002, HIS.

Hierarchical Bottom-Up Safe Semi-Supervised Support Vector Machines for Multi-Class Transductive Learning

André Carlos Ponce de Leon Ferreira de Carvalho, Rodrigo C. Barros, Eduardo R. Hruschka, 2013, J. Inf. Data Manag..

Efficiency issues of evolutionary k-means

André Carlos Ponce de Leon Ferreira de Carvalho, Ricardo J. G. B. Campello, Murilo Coelho Naldi, 2011, Appl. Soft Comput..

A clustering algorithm for extracting rules from supervised neural network models in data mining tasks

Nelson F. F. Ebecken, Eduardo R. Hruschka, N. Ebecken, 2000, Int. J. Comput. Syst. Signals.

Probabilistic Combination of Classifier and Cluster Ensembles for Non-transductive Learning pdf

Jean-David Ruvini, Ayan Acharya, Joydeep Ghosh, 2013, SDM.

An evolutionary algorithm for clustering data streams with a variable number of clusters

João Gama, Eduardo R. Hruschka, Jonathan de Andrade Silva, 2017, Expert Syst. Appl..

Privileged Information for Hierarchical Document Clustering: A Metric Learning Approach

Marcos Aurélio Domingues, Ricardo M. Marcacini, Solange Oliveira Rezende, 2014, 2014 22nd International Conference on Pattern Recognition.

Applying Bayesian Networks for Meteorological Data Mining

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2005, SGAI Conf..

Improving the Efficiency of a Clustering Genetic Algorithm

Ricardo J. G. B. Campello, Leandro Nunes de Castro, Eduardo R. Hruschka, 2004, IBERAMIA.

A Distance-Based Mutation Operator for learning Bayesian Network structures using Evolutionary Algorithms

Estevam R. Hruschka, Nelson F. F. Ebecken, Edimilson Batista dos Santos, 2010, IEEE Congress on Evolutionary Computation.

Document Clustering for Forensic Computing: An Approach for Improving Computer Inspection

Eduardo R. Hruschka, Luís Filipe da Cruz Nassif, 2011, 2011 10th International Conference on Machine Learning and Applications and Workshops.

Bayesian Feature Selection for Clustering Problems

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2006, J. Inf. Knowl. Manag..

Applying a clustering genetic algorithm for extracting rules from a supervised neural network

Nelson F. F. Ebecken, Eduardo R. Hruschka, 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

Fuzzy Clustering-Based Filter

Ricardo J. G. B. Campello, Eduardo R. Hruschka, Luiz F. S. Coletta, 2010, IPMU.

TermitAnt: An Ant Clustering Algorithm Improved by Ideas from Termite Colonies

Leandro Nunes de Castro, Eduardo R. Hruschka, Vahid Sherafat, 2004, ICONIP.

Towards improving cluster-based feature selection with a simplified silhouette filter

Eduardo R. Hruschka, Thiago F. Covoes, E. Hruschka, 2011, Inf. Sci..

Using unsupervised information to improve semi-supervised tweet sentiment classification

Estevam R. Hruschka, Eduardo R. Hruschka, Luiz F. S. Coletta, 2016, Inf. Sci..

Towards the Use of Metaheuristics for Optimizing the Combination of Classifier and Cluster Ensembles

Joydeep Ghosh, Ayan Acharya, Eduardo R. Hruschka, 2013, 2013 BRICS Congress on Computational Intelligence and 11th Brazilian Congress on Computational Intelligence.

Using metaheuristics to optimize the combination of classifier and cluster ensembles

Joydeep Ghosh, Ayan Acharya, Eduardo R. Hruschka, 2015, Integr. Comput. Aided Eng..

Using Meta-learning to Classify Traveling Salesman Problems

André Carlos Ponce de Leon Ferreira de Carvalho, Carlos Soares, Eduardo R. Hruschka, 2010, 2010 Eleventh Brazilian Symposium on Neural Networks.

On the efficiency of evolutionary fuzzy clustering

Ricardo J. G. B. Campello, Eduardo R. Hruschka, Vinicius S. Alves, 2009, J. Heuristics.

Evolutionary Fuzzy Clustering: An Overview and Efficiency Issues

André Carlos Ponce de Leon Ferreira de Carvalho, Ricardo J. G. B. Campello, Murilo Coelho Naldi, 2009, Foundations of Computational Intelligence.

Evaluating a Nearest-Neighbor Method to Substitute Continuous Missing Values

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2003, Australian Conference on Artificial Intelligence.

Feature selection for clustering problems: a hybrid algorithm that iterates between k-means and a Bayesian filter

Estevam R. Hruschka, Nelson F. F. Ebecken, Eduardo R. Hruschka, 2005, Fifth International Conference on Hybrid Intelligent Systems (HIS'05).

Classification with Multi-Modal Classes Using Evolutionary Algorithms and Constrained Clustering

Eduardo R. Hruschka, Thiago F. Covoes, T. Covões, 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

Using Both Latent and Supervised Shared Topics for Multitask Learning

Raymond J. Mooney, Ayan Acharya, Aditya Rawal, 2013, ECML/PKDD.

Contextualised Word Embeddings Based on Transfer Learning to Dialogue Response Generation: a Proposal and Comparisons

Anna Helena Reali Costa, Eduardo R. Hruschka, Thomaz Calasans, 2021, ISEEIE.

Relative clustering validity criteria: A comparative overview

Ricardo J. G. B. Campello, Eduardo R. Hruschka, Lucas Vendramin, 2010 .

A fully sensitive correlation measure for data mining

Ricardo J. G. B. Campello, Eduardo R. Hruschka, E. Hruschka, 2008 .

A Novel Design Specification Distance (DSD) based K-Mean Clustering Performance Evaluation on Engineering Materials' Database

Andrew H. Sung, Ricardo J. G. B. Campello, Alex A. Freitas, 2019 .

On the influence of imputation in classification: practical issues

Eduardo R. Hruschka, Eduardo R. Hruschka, Antonio J.T. Garcia, 2009, J. Exp. Theor. Artif. Intell..

Biocom Usp: Tweet Sentiment Analysis with Adaptive Boosting Ensemble

Eduardo R. Hruschka, E. Hruschka, Nádia Silva, 2014, *SEMEVAL.

Clustering Gene-Expression Data: A Hybrid Approach that Iterates Between k-Means and Evolutionary Search

Ricardo J. G. B. Campello, Eduardo R. Hruschka, L. Castro, 2007 .

Adapting Supervised Feature Selection Methods for Clustering Tasks

Eduardo R. Hruschka, E. Hruschka, 2021 .

Enhancing Designer Knowledge to Dialogue Management: A Comparison between Supervised and Reinforcement Learning Approaches

Anna Helena Reali Costa, Eduardo R. Hruschka, Alex F. Mansano, 2022, Anais do XIX Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2022).