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About

I am a PostDoc Researcher at the Laboratory of Artificial Intelligence and Decision Support (LIAAD - INESC Tec), and an Invited Professor at the Sciences College of the University of Porto (FCUP). I successfully defended my Ph.D. at FCUP in 2017, under the supervision of Professor Luís Torgo. My work was fully funded by a scholarship awarded by FCT (Portuguese Foundation for Science and Technology), and my final dissertation was awarded in the Fraunhofer Portugal Challenge 2017. I obtained a MSc in Architectures, Systems and Networks at the Polytechnic Institute of Oporto (Oporto Engineering Superior Institute) in 2012 and was awarded a merit diploma for outstanding academic performance. My interests include outlier prediction, utility-based regression, imbalanced domains and social media analytics.

Interest
Topics
Details

Details

  • Name

    Nuno Moniz
  • Cluster

    Computer Science
  • Role

    Assistant Researcher
  • Since

    01st June 2013
008
Publications

2021

VEST: Automatic Feature Engineering for Forecasting

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

Publication
CoRR

Abstract

2021

Automated Imbalanced Classification via Meta-learning

Authors
Moniz, N; Cerqueira, V;

Publication
Expert Systems with Applications

Abstract

2021

The Compromise of Data Privacy in Predictive Performance

Authors
Carvalho, T; Moniz, N;

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

Abstract

2020

Imbalanced regression and extreme value prediction

Authors
Ribeiro, RP; Moniz, N;

Publication
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

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

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

Abstract

Supervised
thesis

2020

Optimizing call-center operations with Reinforcement Learning

Author
Cátia Sofia Coutinho Correia

Institution
INESCTEC

2020

Automated Privacy Preserving Strategies

Author
Tânia Margarida Marques Carvalho

Institution
INESCTEC

2019

Payment Default Prediction in Telco Services

Author
Ricardo Dias Azevedo

Institution
UP-FCUP

2019

Anticipation of Perturbances in Telco Services

Author
Tânia Margarida Marques Carvalho

Institution
UP-FCUP

2019

Deep learning approach to customer feedback understanding

Author
Ricardo Garcia Oliveira

Institution
UP-FCUP