Stochastic contextual linear bandits are widely used for sequential decision‐making across many domains. However, in high‐dimensional sparse settings, most candidate features are irrelevant to predicting outcomes, and collecting such data is costly. This study examines various SCLB algorithms combined with feature‐selection methods using a fixed reduced dimensionality. With a synthetic dataset exhibiting 90% feature sparsity, we simulated these algorithms and compared their performance to both their base counterparts and to one another. Our experiments demonstrate that integrating feature selection into posterior‐ and confidence‐based bandits can achieve nearly identical regret while requiring only a fraction of the data‐collection cost.