A new support vector machine for categorical features

Support vector machine (SVM) was originally developed to solve binary classification problems for objects with only continuous features, and it often outperforms other classifiers. However, we often encounter datasets with mixed-type features or categorical features only. This study proposes an efficient SVM for dealing with such datasets. The proposed SVM uses a subset of categorical features and it performs well in most cases, including imbalanced and/or high-dimensional categorical datasets. In particular, it is more efficient than existing SVMs in high-dimensional categorical datasets. To validate its performance, it is applied to simulated datasets and various benchmark datasets, and it is also compared to existing SVMs for categorical features.