There are several other feature selection methods that you will discover while reading through machine learning literature. Some don't even look like feature selection methods as they are embedded into the learning process (not to be confused with the previously mentioned wrappers). Decision trees, for instance, have a feature selection mechanism implanted deep in their core. Other learning methods employ some kind of regularization that punishes model complexity, thus driving the learning process towards good performing models that are still "simple". They do this by decreasing the less impactful features' importance to zero and then dropping them (L1-regularization).

So watch out! Often, the power of machine learning methods has to be attributed to their implanted feature selection method to a great degree.