In many machine learning applications, one must perform feature selection in order to obtain good classification performance. For example, selecting a good feature subset is critical when the sample size is small compared with the dimesionality and noise in the observations. When this is the case, it is necessary to reduce the number of features to avoid modeling noise in the classifier. When t...

Feature selection (FS) is an important research topic in the area of data mining and machine learning. FS aims at dealing with high dimensionality problem. It process selecting relevant features removing irrelevant, redundant noisy ones, intending to obtain best performing subset original without any transformation. This paper provides a comprehensive review literature supplement insights recom...

Supervised feature selection aims to find the signals that best predict a target variable. Typical approaches use measures of correlation or similarity, as seen in filter methods, predictive power learned models, wrapper methods. In both approaches, selected features often have high entropies and are not suitable for compression. This is particular drawback automotive domain where fast communic...