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About

Luis Torgo is an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of PortoPortugal. He is a senior researcher of LIAAD / INESC Tec, and a current member of the board of this research lab.

Luis Torgo is also an invited professor of the Stern Business School of the New York University where he has been collaborating in the last 3 years at the Master of Science in Business Analytics.

He has been doing research in the area of Data Mining and Machine Learning since 1990, and has published over 100 papers in several foruns of these areas. Luis Torgo is the author of the widely acclaimed Data Mining with R book published by CRC Press in 2010 with a strongly revised second edition published in January of 2017. He has been involved in many research projects under different roles and involving different types of organizations.

His current broad research interests revolve around analyzing data from dynamic environments, with a particular focus on time and space-time dependent data sets, in the search for unexpected events. In terms of application domains his research is frequently linked with ecological/biological as well as financial domains.

Luis Torgo main contributions to the state of the art on data mining and machine learning are related with tree-based regression methods and more recently with utility-based forecasting methods.

He has a strong experience of teaching different subjects at different academic levels but also in non-academic settings. He is frequently invited for giving short courses on using R for data mining around the world.

Luis Torgo is the CEO and one of the founding partners of KNOYDA a company devoted to training and consulting within data science.

Interest
Topics
Details

Details

003
Publications

2018

Potential of dissimilatory nitrate reduction pathways in polycyclic aromatic hydrocarbon degradation

Authors
Ribeiro, H; de Sousa, T; Santos, JP; Sousa, AGG; Teixeira, C; Monteiro, MR; Salgado, P; Mucha, AP; Almeida, CMR; Torgo, L; Magalhaes, C;

Publication
Chemosphere

Abstract
This study investigates the potential of an indigenous estuarine microbial consortium to degrade two polycyclic aromatic hydrocarbons (PAHs), naphthalene and fluoranthene, under nitrate-reducing conditions. Two physicochemically diverse sediment samples from the Lima Estuary (Portugal) were spiked individually with 25 mg L-1 of each PAH in laboratory designed microcosms. Sediments without PAHs and autoclaved sediments spiked with PAHs were run in parallel. Destructive sampling at the beginning and after 3, 6, 12, 30 and 63 weeks incubation was performed. Naphthalene and fluoranthene levels decreased over time with distinct degradation dynamics varying with sediment type. Next-generation sequencing (NGS) of 16 S rRNA gene amplicons revealed that the sediment type and incubation time were the main drivers influencing the microbial community structure rather than the impact of PAH amendments. Predicted microbial functional analyses revealed clear shifts and interrelationships between genes involved in anaerobic and aerobic degradation of PAHs and in the dissimilatory nitrate-reducing pathways (denitrification and dissimilatory nitrate reduction to ammonium - DNRA). These findings reinforced by clear biogeochemical denitrification signals (NO3 - consumption, and NH4 + increased during the incubation period), suggest that naphthalene and fluoranthene degradation may be coupled with denitrification and DNRA metabolism. The results of this study contribute to the understanding of the dissimilatory nitrate-reducing pathways and help uncover their involvement in degradation of PAHs, which will be crucial for directing remediation strategies of PAH-contaminated anoxic sediments. © 2018 Elsevier Ltd

2018

How to evaluate sentiment classifiers for Twitter time-ordered data?

Authors
Mozetic, I; Torgo, L; Cerqueira, V; Smailovic, J;

Publication
PLoS ONE

Abstract
Social media are becoming an increasingly important source of information about the public mood regarding issues such as elections, Brexit, stock market, etc. In this paper we focus on sentiment classification of Twitter data. Construction of sentiment classifiers is a standard text mining task, but here we address the question of how to properly evaluate them as there is no settled way to do so. Sentiment classes are ordered and unbalanced, and Twitter produces a stream of time-ordered data. The problem we address concerns the procedures used to obtain reliable estimates of performance measures, and whether the temporal ordering of the training and test data matters. We collected a large set of 1.5 million tweets in 13 European languages. We created 138 sentiment models and out-of-sample datasets, which are used as a gold standard for evaluations. The corresponding 138 in-sample data-sets are used to empirically compare six different estimation procedures: three variants of cross-validation, and three variants of sequential validation (where test set always follows the training set). We find no significant difference between the best cross-validation and sequential validation. However, we observe that all cross-validation variants tend to overestimate the performance, while the sequential methods tend to underestimate it. Standard cross-validation with random selection of examples is significantly worse than the blocked cross-validation, and should not be used to evaluate classifiers in time-ordered data scenarios. © 2018 Mozetic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

2018

Twitter as a Source for Time- and Domain-Dependent Sentiment Lexicons

Authors
Guimarães, N; Torgo, L; Figueira, A;

Publication
Lecture Notes in Social Networks - Social Network Based Big Data Analysis and Applications

Abstract

2018

Current State of the Art to Detect Fake News in Social Media: Global Trendings and Next Challenges

Authors
Figueira, A; Guimaraes, N; Torgo, L;

Publication
Proceedings of the 14th International Conference on Web Information Systems and Technologies

Abstract

2018

Resampling with neighbourhood bias on imbalanced domains

Authors
Branco, P; Torgo, L; Ribeiro, RP;

Publication
EXPERT SYSTEMS

Abstract
Imbalanced domains are an important problem that arises in predictive tasks causing a loss in the performance on the most relevant cases for the user. This problem has been extensively studied for classification problems, where the target variable is nominal. Recently, it was recognized that imbalanced domains occur in several other contexts and for multiple tasks, such as regression tasks, where the target variable is continuous. This paper focuses on imbalanced domains in both classification and regression tasks. Resampling strategies are among the most successful approaches to address imbalanced domains. In this work, we propose variants of existing resampling strategies that are able to take into account the information regarding the neighbourhood of the examples. Instead of performing sampling uniformly, our proposals bias the strategies to reinforce some regions of the data sets. With an extensive set of experiments, we provide evidence of the advantage of introducing a neighbourhood bias in the resampling strategies for both classification and regression tasks with imbalanced data sets.

Supervised
thesis

2017

Prediction and Ranking of Highly Popular Web Content

Author
Nuno Miguel Pereira Moniz

Institution
IES_Outra

2016

Domain Oriented Biclustering Validation

Author
Carlos Alberto Magalhães Leite

Institution
UP-FCUP

2016

Lexicon Expansion System for Domain and Time Oriented Sentiment Analysis

Author
Nuno Ricardo Pinheiro da Silva Guimarães

Institution
UP-FCUP

2016

Utility-based Predictive analytics

Author
Paula Alexandra de Oliveira Branco

Institution
UP-FCUP

2015

Importance Prediction in News Recommender Systems

Author
Nuno Miguel Pereira Moniz

Institution
UP-FCUP