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Detalhes

Detalhes

  • Nome

    Luís Torgo
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2008
007
Publicações

2019

On Feature Selection and Evaluation of Transportation Mode Prediction Strategies

Autores
Etemad, M; Soares, A; Matwin, S; Torgo, L;

Publicação
Proceedings of the Workshops of the EDBT/ICDT 2019 Joint Conference, EDBT/ICDT 2019, Lisbon, Portugal, March 26, 2019.

Abstract

2019

Pre-processing approaches for imbalanced distributions in regression

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

Publicação
NEUROCOMPUTING

Abstract
Imbalanced domains are an important problem frequently arising in real world predictive analytics. A significant body of research has addressed imbalanced distributions in classification tasks, where the target variable is nominal. In the context of regression tasks, where the target variable is continuous, imbalanced distributions of the target variable also raise several challenges to learning algorithms. Imbalanced domains are characterized by: (1) a higher relevance being assigned to the performance on a subset of the target variable values; and (2) these most relevant values being underrepresented on the available data set. Recently, some proposals were made to address the problem of imbalanced distributions in regression. Still, this remains a scarcely explored issue with few existing solutions. This paper describes three new approaches for tackling the problem of imbalanced distributions in regression tasks. We propose the adaptation to regression tasks of random over-sampling and introduction of Gaussian Noise, and we present a new method called WEighted Relevance-based Combination Strategy (WERCS). An extensive set of experiments provides empirical evidence of the advantage of using the proposed strategies and, in particular, the WERCS method. We analyze the impact of different data characteristics in the performance of the methods. A data repository with 15 imbalanced regression data sets is also provided to the research community.

2019

Arbitrage of forecasting experts

Autores
Cerqueira, V; Torgo, L; Pinto, F; Soares, C;

Publicação
Machine Learning

Abstract
Forecasting is an important task across several domains. Its generalised interest is related to the uncertainty and complex evolving structure of time series. Forecasting methods are typically designed to cope with temporal dependencies among observations, but it is widely accepted that none is universally applicable. Therefore, a common solution to these tasks is to combine the opinion of a diverse set of forecasts. In this paper we present an approach based on arbitrating, in which several forecasting models are dynamically combined to obtain predictions. Arbitrating is a metalearning approach that combines the output of experts according to predictions of the loss that they will incur. We present an approach for retrieving out-of-bag predictions that significantly improves its data efficiency. Finally, since diversity is a fundamental component in ensemble methods, we propose a method for explicitly handling the inter-dependence between experts when aggregating their predictions. Results from extensive empirical experiments provide evidence of the method’s competitiveness relative to state of the art approaches. The proposed method is publicly available in a software package. © 2018, The Author(s).

2019

A review on web content popularity prediction: Issues and open challenges

Autores
Moniz, N; Torgo, L;

Publicação
Online Social Networks and Media

Abstract
With the profusion of web content, researchers have avidly studied and proposed new approaches to enable the anticipation of its impact on social media, presenting many distinct approaches throughout the last decade. Diverse approaches have been presented to tackle the problem of web content popularity prediction, including standard classification and regression approaches. Furthermore, these approaches have also taken into consideration distinct scenarios of data availability, where one may target the prediction of popularity before or after the publication of the items, which is highly interesting for different objectives from a user standpoint. This work aims at reviewing previous work and discussing open issues and challenges that could foster impactful research on this topic. Five areas are identified that require further research, covering the full spectrum of the problem: social media data, the learning task, recommendation and evaluation. © 2019 Elsevier B.V.

2019

A Brief Overview on the Strategies to Fight Back the Spread of False Information

Autores
Figueira, A; Guirnaraes, N; Torgo, L;

Publicação
JOURNAL OF WEB ENGINEERING

Abstract
The proliferation of false information on social networks is one of the hardest challenges in today's society, with implications capable of changing users perception on what is a fact or rumor. Due to its complexity, there has been an overwhelming number of contributions from the research community like the analysis of specific events where rumors are spread, analysis of the propagation of false content on the network, or machine learning algorithms to distinguish what is a fact and what is "fake news". In this paper, we identify and summarize some of the most prevalent works on the different categories studied. Finally, we also discuss the methods applied to deceive users and what are the next main challenges of this area.

Teses
supervisionadas

2019

Analyzing and Developing Indicators for Building an Automatic Detector of Unreliable Information in Social Media

Autor
Nuno Ricardo Pinheiro da Silva Guimarães

Instituição
UP-FCUP

2019

Predictive Analytics for Dependent Data

Autor
Mariana Rafaela Figueiredo Ferreira de Oliveira

Instituição
UP-FCUP

2019

Ensembles for Time Series Forecasting

Autor
Vítor Manuel Araújo Cerqueira

Instituição
UP-FEUP

2017

Prediction and Ranking of Highly Popular Web Content

Autor
Nuno Miguel Pereira Moniz

Instituição
IES_Outra

2017

Utility-based Predictive analytics

Autor
Paula Alexandra de Oliveira Branco

Instituição
UP-FCUP