Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection
This provides a criterion for feature selection in data analysis, but it is incremental as it builds on existing MSU methods.
The paper analyzed the multivariate symmetric uncertainty (MSU) measure using statistical simulations to understand how factors like attribute number, cardinalities, and sample size affect it, and discovered a condition that preserves MSU quality to aid dimension reduction.
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.