First-principles calculation has become an indispensable methodology in revealing the working principles of nanoscale electronic devices, but ultra-large supercells are usually required in modeling the devices with critical metal/dielectric interfaces. Traditional density functional theory within the generalized gradient approximation (GGA) suffers from the inaccurate band gap problem when metal oxides are present, but they serve as the core component in resistive random access memory (RRAM), which is a promising path for novel high speed non-volatile memories. To obtain improved oxide band gaps, we applied the efficient GGA-1/2 method for self-energy correction, whose computational load is at the same level as standard GGA. In particular, we have investigated the influence of exchange-correlation functional flavors on the GGA-1/2 band structures, taking four important binary oxide RRAM materials (α-Al2O3, r-TiO2, m-ZrO2 and m-HfO2) as benchmark examples. Five GGA functionals (PBE, PBEsol, PW91, revPBE and AM05) were considered and their band structures were compared in detail. We have found that the performance of GGA-1/2 is comparable to state-of-the-art GW and generally superior to the HSE06 hybrid functional. Among the five GGA functionals, PBEsol yields the best results in general. In addition, the applicability of a single self-energy potential for various GGA-1/2 flavors is discussed. Our work provides a guide to the GGA flavor selection, when applying the GGA-1/2 method to metal oxides.