arXiv:1709.08730
공식 코드 · Papers with Code
Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection
원문 초록
In this paper, 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. We discovered a 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.
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Biological Pathway Guided Gene Selection Through Collaborative Reinforcement Learning
Lightweight Model for Poultry Disease Detection from Fecal Images Using Multi-Color Space Feature Optimization and Machine Learning
Efficient Malware Detection with Optimized Learning on High-Dimensional Features
Optimizing Genetic Algorithms with Multilayer Perceptron Networks for Enhancing TinyFace Recognition
Improving Oral Cancer Outcomes Through Machine Learning and Dimensionality Reduction
Quantum Cognition Machine Learning for Forecasting Chromosomal Instability
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