In this paper, we introduce a new form of meta-feature that is based on a distance-weighted class-homogeneous neighbourhood ratio to facilitate algorithm selection. We show that these new meta-features, while exhibiting a cost advantage, achieve a comparable, and in some cases, higher performance than conventional meta-features. These results were obtained via experiments conducted over artificial datasets and real-world datasets from the UCI repository. We further redefine the algorithm selection problem by advocating that accuracy should be calculated based on the assumption that the population of datasets is uniformly distributed. Finally, in this paper, we provide a new perspective on landmarkers, such that a landmarker corresponds to a tuple (algorithm, metric), and propose the idea of a new family of meta-features.