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Details

  • Name

    Carlos Manuel Soares
  • Role

    External Research Collaborator
  • Since

    01st January 2008
006
Publications

2024

Multidimensional subgroup discovery on event logs

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

Publication
EXPERT SYSTEMS WITH APPLICATIONS

Abstract
Subgroup discovery (SD) aims at finding significant subgroups of a given population of individuals characterized by statistically unusual properties of interest. SD on event logs provides insight into particular behaviors of processes, which may be a valuable complement to the traditional process analysis techniques, especially for low -structured processes. This paper proposes a scalable and efficient method to search significant SD rules on frequent sequences of events, exploiting their multidimensional nature. With this method, it is intended to identify significant subsequences of events where the distribution of values of some target aspect is significantly different than the same distribution for the entire event log. A publicly available real -life event log of a Dutch hospital is used as a running example to demonstrate the applicability of our method. The proposed approach was applied on a real -life case study based on the public transport of a medium size European city (Porto, Portugal), for which the event data consists of 133 million smartcard travel validations from buses, trams and trains. The results include a characterization of mobility flows over multiple aspects, as well as the identification of unexpected behaviors in the flow of commuters (public transport). The generated knowledge provided a useful insight into the behavior of travelers, which can be applied at operational, tactical and strategic business levels, enhancing the current view of the transport services to transport authorities and operators.

2024

Symbolic Data Analysis to Improve Completeness of Model Combination Methods

Authors
Strecht, P; Mendes-Moreira, J; Soares, C;

Publication
ADVANCES IN ARTIFICIAL INTELLIGENCE, AI 2023, PT II

Abstract
A growing number of organizations are adopting a strategy of breaking down large data analysis problems into specific sub-problems, tailoring models for each. However, handling a large number of individual models can pose challenges in understanding organization-wide phenomena. Recent studies focus on using decision trees to create a consensus model by aggregating local decision trees into sets of rules. Despite efforts, the resulting models may still be incomplete, i.e., not able to cover the entire decision space. This paper explores methodologies to tackle this issue by generating complete consensus models from incomplete rule sets, relying on rough estimates of the distribution of independent variables. Two approaches are introduced: synthetic dataset creation followed by decision tree training and a specialized algorithm for creating a decision tree from symbolic data. The feasibility of generating complete decision trees is demonstrated, along with an empirical evaluation on a number of datasets.

2024

Systematic Analysis of the Impact of Label Noise Correction on ML Fairness

Authors
Silva, IOE; Soares, C; Sousa, I; Ghani, R;

Publication
ADVANCES IN ARTIFICIAL INTELLIGENCE, AI 2023, PT II

Abstract
Arbitrary, inconsistent, or faulty decision-making raises serious concerns, and preventing unfair models is an increasingly important challenge in Machine Learning. Data often reflect past discriminatory behavior, and models trained on such data may reflect bias on sensitive attributes, such as gender, race, or age. One approach to developing fair models is to preprocess the training data to remove the underlying biases while preserving the relevant information, for example, by correcting biased labels. While multiple label noise correction methods are available, the information about their behavior in identifying discrimination is very limited. In this work, we develop an empirical methodology to systematically evaluate the effectiveness of label noise correction techniques in ensuring the fairness of models trained on biased datasets. Our methodology involves manipulating the amount of label noise and can be used with fairness benchmarks but also with standard ML datasets. We apply the methodology to analyze six label noise correction methods according to several fairness metrics on standard OpenML datasets. Our results suggest that the Hybrid Label Noise Correction [20] method achieves the best trade-off between predictive performance and fairness. Clustering-Based Correction [14] can reduce discrimination the most, however, at the cost of lower predictive performance.

2024

Detection of Covid-19 in Chest X-Ray Images Using Percolation Features and Hermite Polynomial Classification

Authors
Roberto, GF; Pereira, DC; Martins, AS; Tosta, TAA; Soares, C; Lumini, A; Rozendo, GB; Neves, LA; Nascimento, MZ;

Publication
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I

Abstract
Covid-19 is a serious disease caused by the Sars-CoV-2 virus that has been first reported in China at late 2019 and has rapidly spread around the world. As the virus affects mostly the lungs, chest X-rays are one of the safest and most accessible ways of diagnosing the infection. In this paper, we propose the use of an approach for detecting Covid-19 in chest X-ray images through the extraction and classification of local and global percolation-based features. The method was applied in two datasets: one containing 2,002 segmented samples split into two classes (Covid-19 and Healthy); and another containing 1,125 non-segmented samples split into three classes (Covid-19, Healthy and Pneumonia). The 48 obtained percolation features were given as input to six different classifiers and then AUC and accuracy values were evaluated. We employed the 10-fold cross-validation method and evaluated the lesion sub-types with binary and multiclass classification using the Hermite Polynomial classifier, which had never been employed in this context. This classifier provided the best overall results when compared to other five machine learning algorithms. These results based in the association of percolation features and Hermite polynomial can contribute to the detection of the lesions by supporting specialists in clinical practices.

2023

GASTeN: Generative Adversarial Stress Test Networks

Authors
Cunha, L; Soares, C; Restivo, A; Teixeira, LF;

Publication
ADVANCES IN INTELLIGENT DATA ANALYSIS XXI, IDA 2023

Abstract
Concerns with the interpretability of ML models are growing as the technology is used in increasingly sensitive domains (e.g., health and public administration). Synthetic data can be used to understand models better, for instance, if the examples are generated close to the frontier between classes. However, data augmentation techniques, such as Generative Adversarial Networks (GAN), have been mostly used to generate training data that leads to better models. We propose a variation of GANs that, given a model, generates realistic data that is classified with low confidence by a given classifier. The generated examples can be used in order to gain insights on the frontier between classes. We empirically evaluate our approach on two well-known image classification benchmark datasets, MNIST and Fashion MNIST. Results show that the approach is able to generate images that are closer to the frontier when compared to the original ones, but still realistic. Manual inspection confirms that some of those images are confusing even for humans.

Supervised
thesis

2022

Classificação de termogramas do pé diabético utilizando machine learning

Author
Rita Maria Veiga Almeida e Silva

Institution
UTAD

2022

INTERFACE GRÁFICA WEB DE GESTÃO DE LICENÇAS EPLAN

Author
GUILHERME JORGE RODRIGUES MOREIRA

Institution
IPP-ISEP

2022

Desenvolvimento de backoffice de plataforma de gestão de casamento

Author
Diogo Tomé Martinho Serafim

Institution
UTAD

2022

ESTUDO E AVALIAÇÃO DE EQUIPAMENTOS DE FABRICO DE BAIXO CUSTO POR PROCESSOS TECNOLÓGICOS

Author
NUNO ANDRÉ SILVA ALVES FERNANDES

Institution
IPP-ISEP

2022

Data personal information discovery system

Author
Flávio Joel Sampaio Teixeira

Institution
UTAD