Novel Randomized Feature Selection Algorithms.

Feature selection is the problem of identifying a subset of the most relevant features in the context of model construction. This problem has been well studied and plays a vital role in machine learning. In this paper we present three randomized algorithms for feature selection. They are generic in nature and can be applied for any learning algorithm. Proposed algorithms can be thought of as a random walk in the space of all possible subsets of the features. We demonstrate the generality of our approaches using three different applications. The simulation results show that our feature selection algorithms outperforms some of the best known algorithms existing in the current literature. [ABSTRACT FROM AUTHOR]