Owl Search Algorithm (OSA) is a recently proposed nature-inspired meta-heuristic algorithm which is easily implementable and exhibits great potential for solving continuous optimization problems. In our earlier work, a binary version of owl search algorithm (BOSA), with transfer functions for mapping the continuous solution space into a binary one, has been developed and applied in optimal feature subset selection problem. In our preliminary simulation experiments, it was found that the performance of the solution depends on the type of transfer function used. In this work, an extensive analysis of various types of transfer functions and their respective effects on the selection of optimal feature subset has been studied by simulation experiments with multiple benchmark datasets. Transfer functions of three different families, S-shaped, V-shaped and quadratic, are used for designing eleven BOSA models, each of which is made by combining individual transfer function. The performances of the proposed wrapper based feature subset selection algorithm based on several BOSA models have been evaluated by simulation experiments with twenty datasets for finding out the best model. The best observed BOSA model has also been compared with other similar meta-heuristics algorithms for feature subset selection. Experimental results show that the feature subset selected by BOSA with quadratic transfer function produces the highest classification accuracy with the minimum number of selected features compared to other algorithms.