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Feature construction, encompassing both feature engineering, which involves the manual design of features by domain experts, and representation learning, which refers to the automated discovery of useful data representations during model construction, is a fundamental aspect of machine learning. Its goal is to transform raw data into a more suitable …

The rapid advancements in Machine Learning (ML) and Black-Box Optimization (BBO) have led to an increased reliance on benchmarking data for evaluating and comparing algorithms across diverse domain tasks. However, the effective exploitation of this data is hindered by challenges such as syntactic variability, semantic ambiguity, and lack of standardization. …

Cardiovascular diseases (CVDs) are a leading cause of mortality globally, significantly affecting patient quality of life and imposing considerable demands on healthcare systems. Chronic heart failure (CHF), a common outcome of CVDs, represents a growing health burden with an increasing incidence. Accurate and early identification of CVDs is critical for …

This thesis investigates machine learning methods for analyzing sequential textual data. Sequential textual data refers to text collected in a specific order where the sequence is significant. Examples include (1) sentences, where word usage and order must adhere to the rules of grammar, (2) news reporting on events happening at …

As ecological, agricultural, and biological disciplines face mounting challenges like biodiversity loss, food chain disruption, and climate change, leveraging machine learning (ML) to process complex and heterogeneous data becomes increasingly vital. This dissertation explores the potential of ML in combination with explainability approaches for enhancing research in life sciences, specifically …

This thesis introduces a novel machine learning methodology for automatically assigning metadata to digitized artifacts in cultural heritage. Cultural heritage is an example of a domain that requires expert labeling, with few pre-existing labeled datasets and where simply getting more labeled data is challenging. The societal importance of cultural heritage …

The rapid evolution of sensor technologies, particularly within the Internet of Things (IoT) domain, has led to an era dominated by massive real-time data flows. This transition resulted in the need for novel data processing methodologies that facilitate the shift from traditional offline batch analytics to agile real-time predictions and …

In this thesis, we introduce novel methods for equation discovery (ED), based on the use of probabilistic grammars. ED and symbolic regression address the task of finding a symbolic mathematical model that best describes observed data. Models can be as simple as an algebraic equation or as complex as a …

We are witnessing major shifts in ecosystems due to intense anthropogenic pressures on the environment in recent decades, including rapid climate change, which is one of the strongest drivers of a global biological response. Raptors, which are at the top of the trophic web, are important indicators of ecosystem health. …

News spreads in many patterns, structures, and dynamics that change throughout time. For a variety of reasons, certain news is only covered in a particular area. Language, economy, geography, politics, time zone, and culture are just a few of the many barriers that prevent news from reaching a larger audience. …