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ORCIDFelipe Kenji Nakano
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2020 – today
- 2024
[j8]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:
Predicting adverse long-term neurocognitive outcomes after pediatric intensive care unit admission. Comput. Methods Programs Biomed. 250: 108166 (2024)- [c12]Pedro Ilidio, Alireza Gharahighehi, Felipe Kenji Nakano, Celine Vens:
Personalized Learning in K-12 Education: Exploring Weak-Labels for a Random Forest-based Collaborative Filtering Approach. ALL@AIED 2024: 38-48 - [c11]Anaïs Tack, Siem Buseyne, Changsheng Chen, Robbe D'hondt, Michiel De Vrindt, Alireza Gharahighehi, Sameh Metwaly, Felipe Kenji Nakano, Ann-Sophie Noreillie:
ITEC at BEA 2024 Shared Task: Predicting Difficulty and Response Time of Medical Exam Questions with Statistical, Machine Learning, and Language Models. BEA 2024: 512-521 - [c10]Pedro Ilidio, Ricardo Cerri, Celine Vens, Felipe Kenji Nakano:
Deep forests with tree-embeddings and label imputation for weak-label learning. IJCNN 2024: 1-8 - 2023
- [j7]Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens:
BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractor. IEEE Access 11: 41348-41367 (2023) - [j6]Miguel Romero, Felipe Kenji Nakano, Jorge Finke, Camilo Rocha, Celine Vens:
Leveraging class hierarchy for detecting missing annotations on hierarchical multi-label classification. Comput. Biol. Medicine 152: 106423 (2023) - [j5]Saulo Martiello Mastelini, Felipe Kenji Nakano, Celine Vens, André Carlos Ponce de Leon Ferreira de Carvalho:
Online Extra Trees Regressor. IEEE Trans. Neural Networks Learn. Syst. 34(10): 6755-6767 (2023) - [c9]Klest Dedja, Felipe Kenji Nakano, Celine Vens:
Active Learning for Survival Analysis with Incrementally Disclosed Label Information. IAL@PKDD/ECML 2023: 46-64 - 2022
- [j4]Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens:
Deep tree-ensembles for multi-output prediction. Pattern Recognit. 121: 108211 (2022) - [c8]Alireza Gharahighehi, Felipe Kenji Nakano, Celine Vens:
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering. IntelliSys (1) 2022: 531-545 - [i4]Alireza Gharahighehi, Felipe Kenji Nakano, Celine Vens:
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering. CoRR abs/2203.05954 (2022) - [i3]Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens:
Explaining random forest prediction through diverse rulesets. CoRR abs/2203.15511 (2022) - [i2]Miguel Romero, Felipe Kenji Nakano, Jorge Finke, Camilo Rocha, Celine Vens:
Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification. CoRR abs/2207.06237 (2022) - 2021
- [c7]Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens:
Explaining a Random Survival Forest by Extracting Prototype Rules. PKDD/ECML Workshops (2) 2021: 451-458 - 2020
- [j3]Felipe Kenji Nakano, Ricardo Cerri, Celine Vens:
Active learning for hierarchical multi-label classification. Data Min. Knowl. Discov. 34(5): 1496-1530 (2020) - [c6]Bruna Zamith Santos, Felipe Kenji Nakano, Ricardo Cerri, Celine Vens:
Predictive Bi-clustering Trees for Hierarchical Multi-label Classification. ECML/PKDD (3) 2020: 701-718 - [i1]Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens:
Deep tree-ensembles for multi-output prediction. CoRR abs/2011.02829 (2020)
2010 – 2019
- 2019
- [j2]Felipe Kenji Nakano, Mathias Lietaert, Celine Vens:
Machine learning for discovering missing or wrong protein function annotations - A comparison using updated benchmark datasets. BMC Bioinform. 20(1): 485:1-485:32 (2019) - [j1]Saulo Martiello Mastelini, Victor Guilherme Turrisi da Costa, Everton Jose Santana, Felipe Kenji Nakano, Rodrigo Capobianco Guido, Ricardo Cerri, Sylvio Barbon Junior:
Multi-Output Tree Chaining: An Interpretative Modelling and Lightweight Multi-Target Approach. J. Signal Process. Syst. 91(2): 191-215 (2019) - [c5]Felipe Kenji Nakano, Ricardo Cerri:
Hierarchical Classification of Transposable Elements. BNAIC/BENELEARN 2019 - 2018
- [c4]Bruna Zamith Santos, Gean Trindade Pereira, Felipe Kenji Nakano, Ricardo Cerri:
Strategies for Selection of Positive and Negative Instances in the Hierarchical Classification of Transposable Elements. BRACIS 2018: 420-425 - [c3]Felipe Kenji Nakano, Saulo Martiello Mastelini, Sylvio Barbon, Ricardo Cerri:
Improving Hierarchical Classification of Transposable Elements using Deep Neural Networks. IJCNN 2018: 1-8 - 2017
- [c2]Felipe Kenji Nakano, Saulo Martiello Mastelini, Sylvio Barbon, Ricardo Cerri:
Stacking Methods for Hierarchical Classification. ICMLA 2017: 289-296 - [c1]Felipe Kenji Nakano, Walter José G. S. Pinto, Gisele Lobo Pappa, Ricardo Cerri:
Top-down strategies for hierarchical classification of transposable elements with neural networks. IJCNN 2017: 2539-2546
Coauthor Index
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