Rafael Gomes Mantovani

Orcid: 0000-0001-9564-106X

According to our database1, Rafael Gomes Mantovani authored at least 31 papers between 2015 and 2023.

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

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
Classification of the growth level of fungal colonies in solid medium: a machine learning approach.
Expert Syst. Appl., December, 2023

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

Hyper-parameter initialization of classification algorithms using dynamic time warping: A perspective on PCA meta-features.
Appl. Soft Comput., February, 2023

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

2021
An extensive experimental evaluation of automated machine learning methods for recommending classification algorithms.
Evol. Intell., 2021

OpenML Benchmarking Suites.
Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

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

Time-Series in Hyper-parameter Initialization of Machine Learning Techniques.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2021, 2021

Feature Analysis to League of Legends Victory Prediction on the Picks and Bans Phase.
Proceedings of the 2021 IEEE Conference on Games (CoG), 2021

Learning Abstract Task Representations.
Proceedings of the AAAI Workshop on Meta-Learning and MetaDL Challenge, 2021

2020
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version).
CoRR, 2020

Rethinking Default Values: a Low Cost and Efficient Strategy to Define Hyperparameters.
CoRR, 2020

2019
A meta-learning approach for selecting image segmentation algorithm.
Pattern Recognit. Lett., 2019

A meta-learning recommender system for hyperparameter tuning: Predicting when tuning improves SVM classifiers.
Inf. Sci., 2019

Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization.
EURASIP J. Image Video Process., 2019

Transfer Learning for Algorithm Recommendation.
CoRR, 2019

Towards Meta-Learning for Multi-Target Regression Problems.
Proceedings of the 8th Brazilian Conference on Intelligent Systems, 2019

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

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

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

Dimensionality Reduction for the Algorithm Recommendation Problem.
Proceedings of the 7th Brazilian Conference on Intelligent Systems, 2018

2017
OpenML Benchmarking Suites and the OpenML100.
CoRR, 2017

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

2016
Storage time prediction of pork by Computational Intelligence.
Comput. Electron. Agric., 2016

A Meta-Learning Approach for Recommendation of Image Segmentation Algorithms.
Proceedings of the 29th SIBGRAPI Conference on Graphics, Patterns and Images, 2016

Effects of Random Sampling on SVM Hyper-parameter Tuning.
Proceedings of the Intelligent Systems Design and Applications, 2016

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

2015
Meta-learning Recommendation of Default Hyper-parameter Values for SVMs in Classification Tasks.
Proceedings of the 2015 International Workshop on Meta-Learning and Algorithm Selection co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2015 (ECMLPKDD 2015), 2015

Effectiveness of Random Search in SVM hyper-parameter tuning.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

To tune or not to tune: Recommending when to adjust SVM hyper-parameters via meta-learning.
Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

Pattern Recognition of Lower Member Skin Ulcers in Medical Images with Machine Learning Algorithms.
Proceedings of the 28th IEEE International Symposium on Computer-Based Medical Systems, 2015


  Loading...