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Towards temporal associative classification using triclustering
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Resumo(s)
This thesis presents the development of a temporal associative classifier aimed at addressing
the inherent complexities of biomedical data analysis through the use of discriminative triclusters.
Biomedical datasets are often characterized by high dimensionality, temporal misalignment, and
missing data, which pose significant challenges for traditional classification methods.
The developed classifier leverages triclustering techniques to uncover patterns within threedimensional substructures, particularly focusing on the temporal dimension. These patterns,
identified by triclustering algorithms, are utilized to enhance the accuracy and interpretability of
predictions within clinical contexts.
The research involves a comprehensive evaluation of the classifier’s performance against
established Triclustering-Based approaches using Random Forest and XGBoost classifiers, which
are also designed to handle three-way temporal data. The evaluation metrics include accuracy,
specificity, and sensitivity, carefully chosen to reflect the clinical importance of minimizing both
false positives and false negatives. The results indicate that the associative classifier exhibits
competitive performance, particularly in scenarios where constant, additive, and multiplicative
patterns are predominantly present in the context dimension. However, the study also reveals
limitations, notably the classifier’s dependency on the quality and quantity of triclusters generated,
which significantly influences its overall effectiveness.
Despite these limitations, the classifier demonstrates potential in condensing complex triclusters
into a more interpretable set of rules, thereby enhancing its applicability in clinical settings where
both accuracy and interpretability are critical. The study contributes to the field of temporal data
classification by providing valuable insights into the use of triclustering in biomedical data analysis.
It also provide the basis for future research aimed at refining triclustering algorithms, and expanding
the developed classifier’s capability to capture patterns on the feature dimension.
Ultimately, this research aspires to advance precision medicine by offering a robust tool that can
improve diagnostic accuracy and treatment personalization, thereby positively impacting healthcare
outcomes.
Descrição
Tese de mestrado, Ciência de Dados, 2024, Universidade de Lisboa, Faculdade de Ciências
Palavras-chave
dados biomédicos classificação associativa triclustering dados temporais tridimensionais Teses de mestrado - 2024