Apply a logarithmic transformation to adjust the scale of features, which can be particularly useful for features with exponential distributions.

Employ principal component analysis (PCA) to reduce the dimensionality of the data, thereby indirectly addressing the scale disparity among features.

Normalize the data by scaling all features to a 0 to 1 range, ensuring each feature contributes equally to the model's learning process without altering the original data distribution significantly.

Implement a binning system to replace each feature's value with a corresponding bin number, which simplifies the data but may lead to loss of granularity.