Ricardo Cerri

Orcid: 0000-0002-2582-1695

According to our database1, Ricardo Cerri authored at least 76 papers between 2009 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
A Grammar-based multi-objective neuroevolutionary algorithm to generate fully convolutional networks with novel topologies.
Appl. Soft Comput., December, 2024

Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms.
Data Min. Knowl. Discov., May, 2024

Automated CNN optimization using multi-objective grammatical evolution.
Appl. Soft Comput., January, 2024

Label Cluster Chains for Multi-Label Classification.
CoRR, 2024

Fast Bipartite Forests for Semi-supervised Interaction Prediction.
Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing, 2024

Deep forests with tree-embeddings and label imputation for weak-label learning.
Proceedings of the International Joint Conference on Neural Networks, 2024

Ensemble Methods for Selecting Single Nucleotide Polymorphisms Associated to Rice Phenotypes.
Proceedings of the International Joint Conference on Neural Networks, 2024

Classification of LTR Retrotransposons via Interaction Prediction.
Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, 2024

2023
A systematic literature review on AutoML for multi-target learning tasks.
Artif. Intell. Rev., November, 2023

Multi-label classification via closed frequent labelsets and label taxonomies.
Soft Comput., July, 2023

Novelty detection for multi-label stream classification under extreme verification latency.
Appl. Soft Comput., July, 2023

Semi-Supervised Hybrid Predictive Bi-Clustering Trees for Drug-Target Interaction Prediction.
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing, 2023

A New Time Series Framework for Forest Fire Risk Forecasting and Classification.
Proceedings of the International Joint Conference on Neural Networks, 2023

AutoMMLC: An Automated and Multi-objective Method for Multi-label Classification.
Proceedings of the Intelligent Systems - 12th Brazilian Conference, 2023

Constructive Machine Learning and Hierarchical Multi-label Classification for Molecules Design.
Proceedings of the Intelligent Systems - 12th Brazilian Conference, 2023

Community Detection for Multi-label Classification.
Proceedings of the Intelligent Systems - 12th Brazilian Conference, 2023

2022
A Two-step Model for Drug-Target Interaction Prediction with Predictive Bi-Clustering Trees and XGBoost.
Proceedings of the International Joint Conference on Neural Networks, 2022

An Algorithm Adaptation Method for Multi-Label Stream Classification using Self-Organizing Maps.
Proceedings of the 21st IEEE International Conference on Machine Learning and Applications, 2022

A new grammatical evolution method for generating deep convolutional neural networks with novel topologies.
Proceedings of the GECCO '22: Genetic and Evolutionary Computation Conference, Companion Volume, Boston, Massachusetts, USA, July 9, 2022

2021
The experience of teaching introductory programming skills to bioscientists in Brazil.
PLoS Comput. Biol., 2021

Beyond global and local multi-target learning.
Inf. Sci., 2021

Preventing the generation of inconsistent sets of crisp classification rules.
Expert Syst. Appl., 2021

A new self-organizing map based algorithm for multi-label stream classification.
Proceedings of the SAC '21: The 36th ACM/SIGAPP Symposium on Applied Computing, 2021

Exploring Label Correlations for Partitioning the Label Space in Multi-label Classification.
Proceedings of the International Joint Conference on Neural Networks, 2021

Exploring Autoencoders for Feature Extraction in Multi-Target Classification.
Proceedings of the International Joint Conference on Neural Networks, 2021

Feature Selection for Hierarchical Multi-label Classification.
Proceedings of the Advances in Intelligent Data Analysis XIX, 2021

2020
Active learning for hierarchical multi-label classification.
Data Min. Knowl. Discov., 2020

Multi-label Stream Classification with Self-Organizing Maps.
CoRR, 2020

DSTARS: A multi-target deep structure for tracking asynchronous regressor stacking.
Appl. Soft Comput., 2020

Predictive Bi-clustering Trees for Hierarchical Multi-label Classification.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

Generation of consistent sets of multi-label classification rules with a multi-objective evolutionary algorithm.
Proceedings of the GECCO '20: Genetic and Evolutionary Computation Conference, 2020

2019
Multi-Output Tree Chaining: An Interpretative Modelling and Lightweight Multi-Target Approach.
J. Signal Process. Syst., 2019

Preventing the Generation of Inconsistent Sets of Classification Rules.
CoRR, 2019

Inducing Hierarchical Multi-label Classification rules with Genetic Algorithms.
Appl. Soft Comput., 2019

Pruned Sets for Multi-Label Stream Classification without True Labels.
Proceedings of the International Joint Conference on Neural Networks, 2019

A lexicographic genetic algorithm for hierarchical classification rule induction.
Proceedings of the Genetic and Evolutionary Computation Conference, 2019

Hierarchical Classification of Transposable Elements with a Weighted Genetic Algorithm.
Proceedings of the Progress in Artificial Intelligence, 2019

Novelty Detection for Multi-Label Stream Classification.
Proceedings of the 8th Brazilian Conference on Intelligent Systems, 2019

Hierarchical Classification of Transposable Elements.
Proceedings of the 31st Benelux Conference on Artificial Intelligence (BNAIC 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), 2019

2018
A machine learning based framework to identify and classify long terminal repeat retrotransposons.
PLoS Comput. Biol., 2018

Hierarchical and Non-Hierarchical Classification of Transposable Elements with a Genetic Algorithm.
J. Inf. Data Manag., 2018

frontmatter.
J. Inf. Data Manag., 2018

Applying multi-label techniques in emotion identification of short texts.
Neurocomputing, 2018

An empirical study on hyperparameter tuning of decision trees.
CoRR, 2018

Improving Hierarchical Classification of Transposable Elements using Deep Neural Networks.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Multi-label Feature Selection Techniques for Hierarchical Multi-label Protein Function Prediction.
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Hierarchical Multi-Label Classification Networks.
Proceedings of the 35th International Conference on Machine Learning, 2018

A Genetic Algorithm for Transposable Elements Hierarchical Classification Rule Induction.
Proceedings of the 2018 IEEE Congress on Evolutionary Computation, 2018

Strategies for Selection of Positive and Negative Instances in the Hierarchical Classification of Transposable Elements.
Proceedings of the 7th Brazilian Conference on Intelligent Systems, 2018

2017
Hierarchical multi-label classification with chained neural networks.
Proceedings of the Symposium on Applied Computing, 2017

Top-down strategies for hierarchical classification of transposable elements with neural networks.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

A self-organizing map-based method for multi-label classification.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Incorporating instance correlations in multi-label classification via label-space.
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Stacking Methods for Hierarchical Classification.
Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, 2017

DSTARS: A Multi-target Deep Structure for Tracking Asynchronous Regressor Stack.
Proceedings of the 2017 Brazilian Conference on Intelligent Systems, 2017

2016
Reduction strategies for hierarchical multi-label classification in protein function prediction.
BMC Bioinform., 2016

Hyper-Parameter Tuning of a Decision Tree Induction Algorithm.
Proceedings of the 5th Brazilian Conference on Intelligent Systems, 2016

2015
An Extensive Evaluation of Decision Tree-Based Hierarchical Multilabel Classification Methods and Performance Measures.
Comput. Intell., 2015

Learning HMMs for nucleotide sequences from amino acid alignments.
Bioinform., 2015

Hierarchical classification of Gene Ontology-based protein functions with neural networks.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

2014
Hierarchical multi-label classification using local neural networks.
J. Comput. Syst. Sci., 2014

A framework for bottom-up induction of oblique decision trees.
Neurocomputing, 2014

Evolving relational hierarchical classification rules for predicting gene ontology-based protein functions.
Proceedings of the Genetic and Evolutionary Computation Conference, 2014

2013
Neural networks and genetic algorithms for hierarchical multi-label classification.
PhD thesis, 2013

Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

A grammatical evolution approach for software effort estimation.
Proceedings of the Genetic and Evolutionary Computation Conference, 2013

A grammatical evolution algorithm for generation of Hierarchical Multi-Label Classification rules.
Proceedings of the IEEE Congress on Evolutionary Computation, 2013

Neural Networks for Hierarchical Classification of G-Protein Coupled Receptors.
Proceedings of the Brazilian Conference on Intelligent Systems, 2013

2012
A genetic algorithm for Hierarchical Multi-Label Classification.
Proceedings of the ACM Symposium on Applied Computing, 2012

2011
Adapting non-hierarchical multilabel classification methods for hierarchical multilabel classification.
Intell. Data Anal., 2011

Hierarchical Multilabel Protein Function Prediction Using Local Neural Networks.
Proceedings of the Advances in Bioinformatics and Computational Biology, 2011

Hierarchical multi-label classification for protein function prediction: A local approach based on neural networks.
Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, 2011

A bottom-up oblique decision tree induction algorithm.
Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, 2011

2010
Hierarchical Multilabel Classification Using Top-Down Label Combination and Artificial Neural Networks.
Proceedings of the 11th Brazilian Symposium on Neural Networks (SBRN 2010), 2010

New top-down methods using SVMs for Hierarchical Multilabel Classification problems.
Proceedings of the International Joint Conference on Neural Networks, 2010

2009
Comparing Methods for Multilabel Classification of Proteins Using Machine Learning Techniques.
Proceedings of the Advances in Bioinformatics and Computational Biology, 2009


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