Improved support vector machine algorithm for heterogeneous data
We propose an algorithm to map nominal features to a numerical space via minimizing estimated generalization errors. We integrate the mapping algorithm with support vector machines and result in an improved learning algorithm from heterogeneous data. Experiments show the proposed technique is effective for learning with heterogeneous data and also help deal with imbalanced tasks.
support vector machine heterogeneous data nominal attribute numerical attribute classification learning