32 citations · 32 across the 6 of their papers we have counts for
6 papers
Oxytrees: Model Trees for Bipartite Learning
Pedro Ilídio, Felipe Kenji Nakano, Alireza Gharahighehi +3
Bipartite learning is a machine learning task that aims to predict interactions between pairs of instances. It has been applied to various domains, including drug-target interactio…
Pairwise and Attribute-Aware Decision Tree-Based Preference Elicitation for Cold-Start Recommendation
Alireza Gharahighehi, Felipe Kenji Nakano, Xuehua Yang +2
Recommender systems (RSs) are intelligent filtering methods that suggest items to users based on their inferred preferences, derived from their interaction history on the platform.…
Hierarchy exploitation to detect missing annotations on hierarchical multi-label classification
Miguel Romero, Felipe Kenji Nakano, Jorge Finke +2
The availability of genomic data has grown exponentially in the last decade, mainly due to the development of new sequencing technologies. Based on the interactions between genes (…
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering
Alireza Gharahighehi, Felipe Kenji Nakano, Celine Vens
Recommender systems are information retrieval methods that predict user preferences to personalize services. These systems use the feedback and the ratings provided by users to mod…
BELLATREX: Building Explanations through a LocaLly AccuraTe Rule EXtractor
Klest Dedja, Felipe Kenji Nakano, Konstantinos Pliakos +1
Tree-ensemble algorithms, such as random forest, are effective machine learning methods popular for their flexibility, high performance, and robustness to overfitting. However, sin…
Deep tree-ensembles for multi-output prediction
Felipe Kenji Nakano, Konstantinos Pliakos, Celine Vens
Recently, deep neural networks have expanded the state-of-art in various scientific fields and provided solutions to long standing problems across multiple application domains. Nev…