Felipe Kenji Nakano

According to our database, Felipe Kenji Nakano authored at least 21 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission.

Felipe Kenji Nakano, Karolijn Dulfer, Ilse Vanhorebeek, Pieter J. Wouters, Sascha C. Verbruggen, Koen F. Joosten, Fabian Güiza Grandas, Celine Vens, Greet Van den Berghe

Comput. Methods Programs Biomed., 2024

Deep forests with tree-embeddings and label imputation for weak-label learning.

Pedro Ilidio, Ricardo Cerri, Celine Vens, Felipe Kenji Nakano

Proceedings of the International Joint Conference on Neural Networks, 2024

ITEC at BEA 2024 Shared Task: Predicting Difficulty and Response Time of Medical Exam Questions with Statistical, Machine Learning, and Language Models.

Anaïs Tack, Siem Buseyne, Changsheng Chen, Robbe D'hondt, Michiel De Vrindt, Alireza Gharahighehi, Sameh Metwaly, Felipe Kenji Nakano, Ann-Sophie Noreillie

Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications, 2024

2023

Online Extra Trees Regressor.

Saulo Martiello Mastelini, Felipe Kenji Nakano, Celine Vens, André Carlos Ponce de Leon Ferreira de Carvalho

IEEE Trans. Neural Networks Learn. Syst., October, 2023

Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification.

Miguel Romero, Felipe Kenji Nakano, Jorge Finke, Camilo Rocha, Celine Vens

Comput. Biol. Medicine, 2023

BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractor.

Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

IEEE Access, 2023

Active Learning for Survival Analysis with Incrementally Disclosed Label Information.

Klest Dedja, Felipe Kenji Nakano, Celine Vens

Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2023), 2023

2022

Deep tree-ensembles for multi-output prediction.

Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

Pattern Recognit., 2022

Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification.

Miguel Romero, Felipe Kenji Nakano, Jorge Finke, Camilo Rocha, Celine Vens

CoRR, 2022

Explaining random forest prediction through diverse rulesets.

Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

CoRR, 2022

An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering.

Alireza Gharahighehi, Felipe Kenji Nakano, Celine Vens

Proceedings of the Intelligent Systems and Applications, 2022

2021

Explaining a Random Survival Forest by Extracting Prototype Rules.

Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

2020

Active learning for hierarchical multi-label classification.

Felipe Kenji Nakano, Ricardo Cerri, Celine Vens

Data Min. Knowl. Discov., 2020

Predictive Bi-clustering Trees for Hierarchical Multi-label Classification.

Bruna Zamith Santos, Felipe Kenji Nakano, Ricardo Cerri, Celine Vens

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2020

2019

Multi-Output Tree Chaining: An Interpretative Modelling and Lightweight Multi-Target Approach.

Saulo Martiello Mastelini, Victor Guilherme Turrisi da Costa, Everton Jose Santana, Felipe Kenji Nakano, Rodrigo Capobianco Guido, Ricardo Cerri, Sylvio Barbon Junior

J. Signal Process. Syst., 2019

Machine learning for discovering missing or wrong protein function annotations - A comparison using updated benchmark datasets.

Felipe Kenji Nakano, Mathias Lietaert, Celine Vens

BMC Bioinform., 2019

Hierarchical Classification of Transposable Elements.

Felipe Kenji Nakano, Ricardo Cerri

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

2018

Improving Hierarchical Classification of Transposable Elements using Deep Neural Networks.

Felipe Kenji Nakano, Saulo Martiello Mastelini, Sylvio Barbon, Ricardo Cerri

Proceedings of the 2018 International Joint Conference on Neural Networks, 2018

Strategies for Selection of Positive and Negative Instances in the Hierarchical Classification of Transposable Elements.

Bruna Zamith Santos, Gean Trindade Pereira, Felipe Kenji Nakano, Ricardo Cerri

Proceedings of the 7th Brazilian Conference on Intelligent Systems, 2018

2017

Top-down strategies for hierarchical classification of transposable elements with neural networks.

Felipe Kenji Nakano, Walter José G. S. Pinto, Gisele Lobo Pappa, Ricardo Cerri

Proceedings of the 2017 International Joint Conference on Neural Networks, 2017

Stacking Methods for Hierarchical Classification.

Felipe Kenji Nakano, Saulo Martiello Mastelini, Sylvio Barbon, Ricardo Cerri

Proceedings of the 16th IEEE International Conference on Machine Learning and Applications, 2017