While the KEA algorithm offers advantages in fine-tuning kernel models and improving accuracy without increasing the number of centers, there are potential drawbacks and limitations to consider compared to other kernel optimization techniques. One limitation is the computational complexity of the algorithm, especially as the dataset size increases. The iterative nature of the exchange steps in KEA may lead to longer computation times, particularly for large datasets or high-dimensional data.
Another drawback is the reliance on the initial set of centers provided by the greedy algorithm. If the initial selection of centers is suboptimal, the performance of the KEA algorithm may be limited in achieving significant improvements. Additionally, the effectiveness of KEA may vary depending on the specific characteristics of the dataset and the chosen kernel function, making it less universally applicable compared to some other optimization techniques.
Furthermore, the KEA algorithm may struggle with highly non-linear or complex datasets where simple exchange steps may not be sufficient to capture the underlying patterns effectively. In such cases, more sophisticated optimization methods or kernel functions may be required to achieve better results.