An enhanced approach to Las Vegas Filter (LVF) feature selection algorithm

Real life databases contain many features. Many of these features may be irrelevant or redundant. For example, data recording the age of each teacher in a school is unlikely to help in assessing the success of students' results in the school. Hence, relevant analysis is needed to be performed on the data in order to identify and remove any such irrelevant or redundant attributes from the learning process. This paper explains a Las Vegas feature selection algorithm that makes probabilistic choices to help guide the search more quickly to find a correct set (or sets) of M features. This paper also proposes an enhanced version of Las Vegas algorithm which helps to speed up the running time of the Las Vegas Filter Algorithm.