Feature Selection with Mutual Information for Uncertain Data

In many real-world situations, the data cannot be assumed to be precise. Indeed uncertain data are often encountered, due for example to the imprecision of measurement devices or to continuously moving objects for which the exact position is impossible to obtain. One way to model this uncertainty is to represent each data value as a probability distribution function; recent works show that adequately taking the uncertainty into account generally leads to improved classification performances. Working with such a representation, this paper proposes to achieve feature selection based on mutual information. Experiments on 8 UCI data sets show that the proposed approach is effective to select relevant features.