Estudio de métodos de construcción de ensembles de clasificadores y aplicaciones

La inteligencia artificial se dedica a la creación de sistemas informáticos con un comportamiento inteligente. Dentro de este área el aprendizaje computacional estudia la creación de sistemas que aprenden por sí mismos. Un tipo de aprendizaje computacional es el aprendizaje supervisado, en el cual, se le proporcionan al sistema tanto las entradas como la salida esperada y el sistema aprende a partir de estos datos. Un sistema de este tipo se denomina clasificador. En ocasiones ocurre, que en el conjunto de ejemplos que utiliza el sistema para aprender, el número de ejemplos de un tipo es mucho mayor que el número de ejemplos de otro tipo. Cuando esto ocurre se habla de conjuntos desequilibrados. La combinación de varios clasificadores es lo que se denomina "ensemble", y a menudo ofrece mejores resultados que cualquiera de los miembros que lo forman. Una de las claves para el buen funcionamiento de los ensembles es la diversidad. Esta tesis, se centra en el desarrollo de nuevos algoritmos de construcción de ensembles, centrados en técnicas de incremento de la diversidad y en los problemas desequilibrados. Adicionalmente, se aplican estas técnicas a la solución de varias problemas industriales.Ministerio de Economía y Competitividad, proyecto TIN-2011-2404

Enhancing Machine Learning Performance with Continuous In-Session Ground Truth Scores: Pilot Study on Objective Skeletal Muscle Pain Intensity Prediction

Machine learning (ML) models trained on subjective self-report scores struggle to objectively classify pain accurately due to the significant variance between real-time pain experiences and recorded scores afterwards. This study developed two devices for acquisition of real-time, continuous in-session pain scores and gathering of ANS-modulated endodermal activity (EDA).The experiment recruited N = 24 subjects who underwent a post-exercise circulatory occlusion (PECO) with stretch, inducing discomfort. Subject data were stored in a custom pain platform, facilitating extraction of time-domain EDA features and in-session ground truth scores. Moreover, post-experiment visual analog scale (VAS) scores were collected from each subject. Machine learning models, namely Multi-layer Perceptron (MLP) and Random Forest (RF), were trained using corresponding objective EDA features combined with in-session scores and post-session scores, respectively. Over a 10-fold cross-validation, the macro-averaged geometric mean score revealed MLP and RF models trained with objective EDA features and in-session scores achieved superior performance (75.9% and 78.3%) compared to models trained with post-session scores (70.3% and 74.6%) respectively. This pioneering study demonstrates that using continuous in-session ground truth scores significantly enhances ML performance in pain intensity characterization, overcoming ground truth sparsity-related issues, data imbalance, and high variance. This study informs future objective-based ML pain system training.Comment: 18 pages, 2-page Appendix, 7 figure

An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results

In Ordinal Classification tasks, items have to be assigned to classes that have a relative ordering, such as positive, neutral, negative in sentiment analysis. Remarkably, the most popular evaluation metrics for ordinal classification tasks either ignore relevant information (for instance, precision/recall on each of the classes ignores their relative ordering) or assume additional information (for instance, Mean Average Error assumes absolute distances between classes). In this paper we propose a new metric for Ordinal Classification, Closeness Evaluation Measure, that is rooted on Measurement Theory and Information Theory. Our theoretical analysis and experimental results over both synthetic data and data from NLP shared tasks indicate that the proposed metric captures quality aspects from different traditional tasks simultaneously. In addition, it generalizes some popular classification (nominal scale) and error minimization (interval scale) metrics, depending on the measurement scale in which it is instantiated.Comment: To appear in Proceedings of ACL 202