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  • Name

    Carlos Manuel Soares
  • Cluster

    Computer Science
  • Role

    External Research Collaborator
  • Since

    01st January 2008
006
Publications

2019

Data mining based framework to assess solution quality for the rectangular 2D strip-packing problem

Authors
Júnior, AN; Silva, E; Gomes, AM; Soares, C; Oliveira, JF;

Publication
Expert Syst. Appl.

Abstract

2019

Data mining based framework to assess solution quality for the rectangular 2D strip-packing problem

Authors
Neuenfeldt Junior, A; Silva, E; Gomes, M; Soares, C; Oliveira, JF;

Publication
Expert Systems with Applications

Abstract
In this paper, we explore the use of reference values (predictors) for the optimal objective function value of hard combinatorial optimization problems, instead of bounds, obtained by data mining techniques, and that may be used to assess the quality of heuristic solutions for the problem. With this purpose, we resort to the rectangular two-dimensional strip-packing problem (2D-SPP), which can be found in many industrial contexts. Mostly this problem is solved by heuristic methods, which provide good solutions. However, heuristic approaches do not guarantee optimality, and lower bounds are generally used to give information on the solution quality, in particular, the area lower bound. But this bound has a severe accuracy problem. Therefore, we propose a data mining-based framework capable of assessing the quality of heuristic solutions for the 2D-SPP. A regression model was fitted by comparing the strip height solutions obtained with the bottom-left-fill heuristic and 19 predictors provided by problem characteristics. Random forest was selected as the data mining technique with the best level of generalisation for the problem, and 30,000 problem instances were generated to represent different 2D-SPP variations found in real-world applications. Height predictions for new problem instances can be found in the regression model fitted. In the computational experimentation, we demonstrate that the data mining-based framework proposed is consistent, opening the doors for its application to finding predictions for other combinatorial optimisation problems, in particular, other cutting and packing problems. However, how to use a reference value instead of a bound, has still a large room for discussion and innovative ideas. Some directions for the use of reference values as a stopping criterion in search algorithms are also provided. © 2018 Elsevier Ltd

2019

Arbitrage of forecasting experts

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

Publication
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).

2018

Metalearning and Recommender Systems: A literature review and empirical study on the algorithm selection problem for Collaborative Filtering

Authors
Cunha, T; Soares, C; de Carvalho, ACPLF;

Publication
INFORMATION SCIENCES

Abstract
The problem of information overload motivated the appearance of Recommender Systems. From the several open problems in this area, the decision of which is the best recommendation algorithm for a specific problem is one of the most important and less studied. The current trend to solve this problem is the experimental evaluation of several recommendation algorithms in a handful of datasets. However, these studies require an extensive amount of computational resources, particularly processing time. To avoid these drawbacks, researchers have investigated the use of Metalearning to select the best recommendation algorithms in different scopes. Such studies allow to understand the relationships between data characteristics and the relative performance of recommendation algorithms, which can be used to select the best algorithm(s) for a new problem. The contributions of this study are two-fold: 1) to identify and discuss the key concepts of algorithm selection for recommendation algorithms via a systematic literature review and 2) to perform an experimental study on the Metalearning approaches reviewed in order to identify the most promising concepts for automatic selection of recommendation algorithms.

2018

Preference rules for label ranking: Mining patterns in multi-target relations

Authors
de Sa, CR; Azevedo, P; Soares, C; Jorge, AM; Knobbe, A;

Publication
INFORMATION FUSION

Abstract
In this paper, we investigate two variants of association rules for preference data, Label Ranking Association Rules and Pairwise Association Rules. Label Ranking Association Rules (LRAR) are the equivalent of Class Association Rules (CAR) for the Label Ranking task. In CAR, the consequent is a single class, to which the example is expected to belong to. In LRAR, the consequent is a ranking of the labels. The generation of LRAR requires special support and confidence measures to assess the similarity of rankings. In this work, we carry out a sensitivity analysis of these similarity-based measures. We want to understand which datasets benefit more from such measures and which parameters have more influence in the accuracy of the model. Furthermore, we propose an alternative type of rules, the Pairwise Association Rules (PAR), which are defined as association rules with a set of pairwise preferences in the consequent. While PAR can be used both as descriptive and predictive models, they are essentially descriptive models. Experimental results show the potential of both approaches.

Supervised
thesis

2017

Model Management for Recommender Systems using Metalearning

Author
Tiago Daniel Sá Cunha

Institution
UP-FEUP

2017

Infraestrutura de Big Data para manutenção ferroviária: um caso de estudo

Author
João Manuel Rodrigues Simões

Institution
UP-FEUP

2017

Sequence Mining Analysis on Shopping Data

Author
João Miguel da Rocha Ribeiro

Institution
UP-FEUP

2017

Entity Retrieval and Text Mining for Online Reputation Monitoring

Author
Pedro dos Santos Saleiro da Cruz

Institution
UP-FEUP

2017

Automatic Recommendation of Machine Learning Workflows

Author
Miguel Alexandre Viana Cachada

Institution
UP-FEP