Q:

What are the ways of pre-processing categorical data before applying classification algorithms? 

I am new to machine learning and I am working on a classification problem with Categorical (nominal) data.  I have tried applying BayesNet and a couple of Trees and Rules classification algorithms to the raw data.  I am able to achieve an AUC of 0.85. 
I further want to improve the AUC by pre-processing or transforming the data.  However since the data is categorical I don't think that log transform, addition, multiplication etc.  of different columns will work here. 
Can somebody list down what are most common transformations applied on categorical data-sets? (  I tried one-hot encoding but it takes a lot of memory!!) 

A:

Categorical is in my experience best dealt with one-hot encoding (e.g converting to a binary vector) as you've mentioned.  If memory is an issue, it may be worthwhile using an online classification algorithm and generate the modified vectors on the fly. 
Apart from this, if the categories represent a range (for example, if the categories represent a range of values such as age, height or income) it may be possible to treat the centre (or some appropriate mean, if there's an intra-label distribution) of the category ranges as a real number. 
If you were applying clustering you could also treat the categorical labels as points on an axis (1,2,3,4,5 etc), scaled appropriately to the other features. 