  Exploring search spaces is one of the most unpredictable challenges that has
attracted the interest of researchers for decades. One way to handle
unpredictability is to characterise the search spaces and take actions
accordingly. A well-characterised search space can assist in mapping the
problem states to a set of operators for generating new problem states. In this
paper, a landscape analysis-based set of features has been analysed using the
most renown machine learning approaches to determine the optimal feature set.
However, in order to deal with problem complexity and induce commonality for
transferring experience across domains, the selection of the most
representative features remains crucial. The proposed approach analyses the
predictivity of a set of features in order to determine the best
categorization.
