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Chapter and Conference Paper
An Adaptive Hybrid Active Learning Strategy with Free Ratings in Collaborative Filtering
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 model the behavior of users and ...
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Chapter and Conference Paper
Explaining a Random Survival Forest by Extracting Prototype Rules
Tree-ensemble algorithms and specifically Random Survival Forests (RSF) have emerged as prominently powerful methods for survival data analysis. Tree-ensembles are very accurate, robust, resilient to overfitti...
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Article
Active learning for hierarchical multi-label classification
Due to technological advances, a massive amount of data is produced daily, presenting challenges for application areas where data needs to be labelled by a domain specialist or by expensive procedures, in orde...
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Article
Open AccessMachine learning for discovering missing or wrong protein function annotations
A massive amount of proteomic data is generated on a daily basis, nonetheless annotating all sequences is costly and often unfeasible. As a countermeasure, machine learning methods have been used to automatica...
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Article
Multi-Output Tree Chaining: An Interpretative Modelling and Lightweight Multi-Target Approach
Multi-target regression (MTR) regards predictive problems with multiple numerical targets. To solve this, machine learning techniques can model solutions treating each target as a separated problem based only ...