The chapter introduces the growing challenges posed by high-dimensional data in machine learning applications and emphasizes the significance of feature selection (FS) to enhance classification performance. Key issues and challenges for high-dimensional machine learning are outlined, including the curse of dimensionality, overfitting, interpretability and scalability. The rationale of using computational intelligence based feature selection approaches to improve the scalability and interpretability of the learning model is concluded.