Search Results for author: Felipe Kenji Nakano

Found 4 papers, 0 papers with code

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

no code implementations13 Jul 2022 Miguel Romero, Felipe Kenji Nakano, Jorge Finke, Camilo Rocha, Celine Vens

The availability of genomic data has grown exponentially in the last decade, mainly due to the development of new sequencing technologies.

Hierarchical Multi-label Classification

Explaining random forest prediction through diverse rulesets

no code implementations29 Mar 2022 Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

Tree-ensemble algorithms, such as random forest, are effective machine learning methods popular for their flexibility, high performance, and robustness to overfitting.

Multi-Label Classification

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

no code implementations11 Mar 2022 Alireza Gharahighehi, Felipe Kenji Nakano, Celine Vens

This rating elicitation procedure enriches the interaction matrix with informative ratings and therefore assists the recommender system to better model the preferences of the users.

Active Learning Collaborative Filtering +2

Deep tree-ensembles for multi-output prediction

no code implementations3 Nov 2020 Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens

In this paper, we specifically focus on two structured output prediction tasks, namely multi-label classification and multi-target regression.

Classification General Classification +3

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