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math.ST updates on arXiv.org

What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning 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Representatividad Muestral en la Incertidumbre Simétrica Multivariada para la Selección de Atributos
Gustavo Sosa-Cabrera · 2024-03-27 · via math.ST updates on arXiv.org

In this work, we analyze the behavior of the multivariate symmetric uncertainty (MSU) measure through the use of statistical simulation techniques under various mixes of informative and non-informative randomly generated features. Experiments show how the number of attributes, their cardinalities, and the sample size affect the MSU. In this thesis, through observation of results, it is proposed an heuristic condition that preserves good quality in the MSU under different combinations of these three factors, providing a new useful criterion to help drive the process of dimension reduction. -- En el presente trabajo hemos analizado el comportamiento de una versión multivariada de la incertidumbre simétrica a través de técnicas de simulación estadísticas sobre varias combinaciones de atributos informativos y no-informativos generados de forma aleatoria. Los experimentos muestran como el número de atributos, sus cardinalidades y el tamaño muestral afectan al MSU como medida. En esta tesis, mediante la observación de resultados hemos propuesto una condición que preserva una buena calidad en el MSU bajo diferentes combinaciones de los tres factores mencionados, lo cual provee un nuevo y valioso criterio para llevar a cabo el proceso de reducción de dimensionalidad.

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