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de interesse
Detalhes

Detalhes

  • Nome

    Nuno Moniz
  • Cluster

    Informática
  • Cargo

    Investigador Auxiliar
  • Desde

    01 junho 2013
008
Publicações

2021

VEST: Automatic Feature Engineering for Forecasting

Autores
Cerqueira, V; Moniz, N; Soares, C;

Publicação
CoRR

Abstract

2021

Automated Imbalanced Classification via Meta-learning

Autores
Moniz, N; Cerqueira, V;

Publicação
Expert Systems with Applications

Abstract

2021

The Compromise of Data Privacy in Predictive Performance

Autores
Carvalho, T; Moniz, N;

Publicação
Advances in Intelligent Data Analysis XIX - Lecture Notes in Computer Science

Abstract

2020

Imbalanced regression and extreme value prediction

Autores
Ribeiro, RP; Moniz, N;

Publicação
Machine Learning

Abstract
Research in imbalanced domain learning has almost exclusively focused on solving classification tasks for accurate prediction of cases labelled with a rare class. Approaches for addressing such problems in regression tasks are still scarce due to two main factors. First, standard regression tasks assume each domain value as equally important. Second, standard evaluation metrics focus on assessing the performance of models on the most common values of data distributions. In this paper, we present an approach to tackle imbalanced regression tasks where the objective is to predict extreme (rare) values. We propose an approach to formalise such tasks and to optimise/evaluate predictive models, overcoming the factors mentioned and issues in related work. We present an automatic and non-parametric method to obtain relevance functions, building on the concept of relevance as the mapping of target values into non-uniform domain preferences. Then, we propose SERA, a new evaluation metric capable of assessing the effectiveness and of optimising models towards the prediction of extreme values while penalising severe model bias. An experimental study demonstrates how SERA provides valid and useful insights into the performance of models in imbalanced regression tasks. © 2020, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature.

2020

Sequence Mining for Automatic Generation of Software Tests from GUI Event Traces

Autores
Oliveira, A; Freitas, R; Jorge, A; Amorim, V; Moniz, N; Paiva, ACR; Azevedo, PJ;

Publicação
Intelligent Data Engineering and Automated Learning - IDEAL 2020 - 21st International Conference, Guimaraes, Portugal, November 4-6, 2020, Proceedings, Part II

Abstract

Teses
supervisionadas

2020

Optimizing call-center operations with Reinforcement Learning

Autor
Cátia Sofia Coutinho Correia

Instituição
INESCTEC

2020

Automated Privacy Preserving Strategies

Autor
Tânia Margarida Marques Carvalho

Instituição
INESCTEC

2019

Payment Default Prediction in Telco Services

Autor
Ricardo Dias Azevedo

Instituição
UP-FCUP

2019

Anticipation of Perturbances in Telco Services

Autor
Tânia Margarida Marques Carvalho

Instituição
UP-FCUP

2019

Deep learning approach to customer feedback understanding

Autor
Ricardo Garcia Oliveira

Instituição
UP-FCUP