A Comparative Study of Supervised Learning Algorithms for High-Dimensional Data
The comparative analysis indicates that there is no universal algorithm that has the best performance in all high-dimensional conditions; the performance highly depends on the sample size, the features correlation, the level of data distribution as well as noise.