Investigating the influence of feature correlations on automatic relevance determination

Feature selection is the technique commonly used in machine learning to select a subset of relevant features for building robust learning models. Ensemble feature relevance determination can properly group the most relevant features together and separate the relevant features from the irrelevant and redundant features. However, it cannot provide reliable local feature relevance rank. In this paper, we demonstrate that the predicted local relevance rank for the relevant features could be influenced by their highly correlated redundant features, according to the strength of their correlations.