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Detalhes

Detalhes

  • Nome

    Carlos Manuel Soares
  • Cluster

    Informática
  • Cargo

    Investigador Colaborador Externo
  • Desde

    01 janeiro 2008
006
Publicações

2020

Building Robust Prediction Models for Defective Sensor Data Using Artificial Neural Networks

Autores
de Sa, CR; Shekar, AK; Ferreira, H; Soares, C;

Publicação
Advances in Intelligent Systems and Computing - 14th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2019)

Abstract

2020

Process discovery on geolocation data

Autores
Ribeiro, J; Fontes, T; Soares, C; Borges, JL;

Publicação
Transportation Research Procedia

Abstract

2019

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

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

Publicação
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

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

KnowBots: Discovering Relevant Patterns in Chatbot Dialogues

Autores
Rivolli, A; Amaral, C; Guardão, L; de Sá, CR; Soares, C;

Publicação
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Chatbots have been used in business contexts as a new way of communicating with customers. They use natural language to interact with the customers, whether while offering products and services, or in the support of a specific task. In this context, an important and challenging task is to assess the effectiveness of the machine-to-human interaction, according to business’ goals. Although several analytic tools have been proposed to analyze the user interactions with chatbot systems, to the best of our knowledge they do not consider user-defined criteria, focusing on metrics of engagement and retention of the system as a whole. For this reason, we propose the KnowBots tool, which can be used to discover relevant patterns in the dialogues of chatbots, by considering specific business goals. Given the non-trivial structure of dialogues and the possibly large number of conversational records, we combined sequential pattern mining and subgroup discovery techniques to identify patterns of usage. Moreover, a friendly user-interface was developed to present the results and to allow their detailed analysis. Thus, it may serve as an alternative decision support tool for business or any entity that makes use of this type of interactions with their clients. © Springer Nature Switzerland AG 2019.

Teses
supervisionadas

2019

Learning to Rank with Random Forest: A Case Study in Hostel Reservations

Autor
Carolina Macedo Moreira

Instituição
UP-FEUP

2019

sistema de apoio à escolha de algoritmos para problemas de optimização

Autor
Pedro Manuel Correia de Abreu

Instituição
UP-FEUP

2019

Prescriptive Analytics for Staff Scheduling Optimization in Retail

Autor
Catarina Alexandra Teixeira Ramos

Instituição
UP-FEUP

2019

Recommending Recommender Systems: tackling the Collaborative Filtering algorithm selection problem

Autor
Tiago Daniel Sá Cunha

Instituição
UP-FEUP

2019

An optimization-based wrapper approach for utility-based data mining

Autor
José Francisco Cagigal da Silva Gomes

Instituição
UP-FEUP