Proceedings of KDNet Symposium on Knowledge-based systems for the Public Sector, , Functional models for regression tree leaves. L Torgo. List of computer science publications by Luís Torgo. Luis Torgo is an Associate Professor of the Department of Computer Science of the Faculty of Sciences of the University of Porto, Portugal. He is a senior.

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He has been doing research in the area of Data Mining and Machine Learning sinceand has published over papers in several foruns of these areas.

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.

Luís Torgo

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. Abstract This study investigates the potential of an indigenous estuarine microbial consortium to degrade two polycyclic aromatic hydrocarbons PAHsnaphthalene and fluoranthene, under luiz 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.

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 torgk 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.

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The results luos 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. 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.

Luis Torgo – CRC Press Online

We collected a large set of 1. We created sentiment models toro out-of-sample datasets, which are used as a gold standard for evaluations. The corresponding in-sample data-sets are used to empirically compare six different toorgo procedures: 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 lyis is significantly worse than the blocked cross-validation, and should not be used to evaluate classifiers in time-ordered data scenarios. This is an open access article distributed under the terms of the Torto Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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.


dblp: Luís Torgo

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.

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Last modified: July 6, 2020