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de interesse
Detalhes

Detalhes

  • Nome

    Luís Torgo
  • Cluster

    Informática
  • Cargo

    Investigador Sénior
  • Desde

    01 janeiro 2008
007
Publicações

2020

Analysis and Detection of Unreliable Users in Twitter: Two Case Studies

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

Publicação
Communications in Computer and Information Science - Knowledge Discovery, Knowledge Engineering and Knowledge Management

Abstract

2020

Wise Sliding Window Segmentation: A Classification-Aided Approach for Trajectory Segmentation

Autores
Etemad, M; Etemad, Z; Soares, A; Bogorny, V; Matwin, S; Torgo, L;

Publicação
Advances in Artificial Intelligence - 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, Ottawa, ON, Canada, May 13-15, 2020, Proceedings

Abstract

2020

Visual interpretation of regression error

Autores
Areosa, I; Torgo, L;

Publicação
Expert Systems

Abstract
Several sophisticated machine learning tools (e.g., ensembles or deep networks) have shown outstanding performance in different regression forecasting tasks. In many real world application domains the numeric predictions of the models drive important and costly decisions. Nevertheless, decision makers frequently require more than a black box model to be able to “trust” the predictions up to the point that they base their decisions on them. In this context, understanding these black boxes has become one of the hot topics in Machine Learning research. This paper proposes a series of visualization tools that explain the relationship between the expected predictive performance of black box regression models and the values of the input variables of any given test case. This type of information thus allows end-users to correctly assess the risks associated with the use of a model, by showing how concrete values of the predictors may affect the performance of the model. Our illustrations with different real world data sets and learning algorithms provide insights on the type of usage and information these tools bring to both the data analyst and the end-user. Furthermore, a thorough evaluation of the proposed tools is performed to showcase the reliability of this approach. © 2020 John Wiley & Sons, Ltd

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.

Teses
supervisionadas

2019

Ensembles for Time Series Forecasting

Autor
Vítor Manuel Araújo Cerqueira

Instituição
UP-FEUP

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

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