32 citations · 67 across the 12 of their papers we have counts for
13 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.…
Enhancing Collaborative Filtering-Based Course Recommendations by Exploiting Time-to-Event Information with Survival Analysis
Alireza Gharahighehi, Achilleas Ghinis, Michela Venturini +2
Massive Open Online Courses (MOOCs) are emerging as a popular alternative to traditional education, offering learners the flexibility to access a wide range of courses from various…
Label Cluster Chains for Multi-Label Classification
Elaine Cecília Gatto, Felipe Nakano Kenji, Jesse Read +3
Multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance. To solve this task, some methods divide the origi…
HypeRS: Building a Hypergraph-driven ensemble Recommender System
Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos
Recommender systems are designed to predict user preferences over collections of items. These systems process users' previous interactions to decide which items should be ranked hi…
Predicting Survival Outcomes in the Presence of Unlabeled Data
Fateme Nateghi Haredasht, Celine Vens
Many clinical studies require the follow-up of patients over time. This is challenging: apart from frequently observed drop-out, there are often also organizational and financial c…
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…
Diversification in Session-based News Recommender Systems
Alireza Gharahighehi, Celine Vens
Recommender systems are widely applied in digital platforms such as news websites to personalize services based on user preferences. In news websites most of users are anonymous an…
Drug-Target Interaction Prediction via an Ensemble of Weighted Nearest Neighbors with Interaction Recovery
Bin Liu, Konstantinos Pliakos, Celine Vens +1
Predicting drug-target interactions (DTI) via reliable computational methods is an effective and efficient way to mitigate the enormous costs and time of the drug discovery process…
Fair Multi-Stakeholder News Recommender System with Hypergraph ranking
Alireza Gharahighehi, Celine Vens, Konstantinos Pliakos
Recommender systems are typically designed to fulfill end user needs. However, in some domains the users are not the only stakeholders in the system. For instance, in a news aggreg…
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…