What are the Challenges of Using SVMs in Cancer Research?
Despite their advantages, SVMs also face certain challenges:
Computational Complexity: Training SVMs can be computationally intensive, especially with large datasets. Choice of Kernel: Selecting the appropriate kernel and tuning its parameters can be challenging and may require expert knowledge. Data Imbalance: Cancer datasets often suffer from class imbalance, where one class is significantly underrepresented. This can affect the performance of SVMs, requiring additional techniques like data resampling or the use of different performance metrics.
