Improved support vector machine algorithm for heterogeneous data

Shili Peng, Qinghua Hu, Yinli Chen, Jianwu Dang

Abstract: Highlights•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.