The primary goal for supervised machine learning techniques is to make accurate predictions or classifications based on prior knowledge about the relationships between the input features and their corresponding label assignment. The latter is often difficult to be determine, especially when the ground truth is not unique but instead, a combined decision from a group of experts is given. One way to tackle this challenge is by using Multiple Expert Learning (MEL)-driven classifiers. In this regard, approaches based on Soft-margin support vector machines (SM-SVM) including penalty factors for each labeler have shown to be a suitable alternative as they can learn from experts holding different levels of trustworthiness. Nonetheless, dealing with nonlinear (complex structure) and hardly separable data is still an open issue. To address these problems, this work presents mathematical developments on SVM for MEL scenarios in a fully matrix formulation. As a remarkable contribution, we introduce a kernel extension of the SM-SVM problem for MEL scenarios together with a complete solution following from a quadratic programming approach.

Dimensionality reduction (DR) approaches are often a crucial step in data analysis tasks, particularly for data visualization purposes. DR-based techniques are essentially designed to retain the inherent structure of high-dimensional data in a lower-dimensional space, leading to reduced computational complexity and improved pattern recognition accuracy. Specifically, Kernel Principal Component Analysis (KPCA) is a widely utilized dimensionality reduction technique due to its capability to effectively handle nonlinear data sets. It offers an easily interpretable formulation from both geometric and functional analysis perspectives. However, Kernel PCA relies on free hyperparameters, which are usually tuned in advance. The relationship between these hyperparameters and the structure of the embedded space remains undisclosed. This work presents preliminary steps to explore said relationship by jointly evaluating the data classification and representation abilities. To do so, an interactive visualization framework is introduced. This study highlights the importance of creating interactive interfaces that enable interpretable dimensionality reduction approaches for data visualization and analysis.

The generation of schedules is a complex challenge, particularly in academic institutions aiming for equitable scheduling. The goal is to achieve fair and balanced schedules that meet the requirements of all parties involved, such as workload, class distribution, shifts, and other relevant criteria. To address this challenge, a genetic algorithm specifically designed for optimal schedule generation has been proposed as a solution. Adjusting genetic algorithm parameters impacts performance, and employing parameter optimization techniques effectively tackles this issue. This work introduces a genetic algorithm for optimal schedule generation, utilizing suitable encoding and operators, and evaluating quality through fitness techniques. Optimization efforts led to reduced execution time, improved solution quality, and positive outcomes like faster execution, fewer generations, increased stability, and convergence to optimal solutions.