Mutual Information-Based Unsupervised Feature Transformation for Heterogeneous Feature Subset Selection
This addresses a specific bottleneck in feature selection for machine learning practitioners dealing with heterogeneous data, though it appears incremental as it builds on existing MI-based methods.
The paper tackled the problem of handling heterogeneous feature subset selection by developing an unsupervised feature transformation (UFT) method that converts non-numerical features into numerical ones, and simulations showed that the integrated UFT-PWFS method outperformed other methods in classification accuracy on large-scale datasets.
Conventional mutual information (MI) based feature selection (FS) methods are unable to handle heterogeneous feature subset selection properly because of data format differences or estimation methods of MI between feature subset and class label. A way to solve this problem is feature transformation (FT). In this study, a novel unsupervised feature transformation (UFT) which can transform non-numerical features into numerical features is developed and tested. The UFT process is MI-based and independent of class label. MI-based FS algorithms, such as Parzen window feature selector (PWFS), minimum redundancy maximum relevance feature selection (mRMR), and normalized MI feature selection (NMIFS), can all adopt UFT for pre-processing of non-numerical features. Unlike traditional FT methods, the proposed UFT is unbiased while PWFS is utilized to its full advantage. Simulations and analyses of large-scale datasets showed that feature subset selected by the integrated method, UFT-PWFS, outperformed other FT-FS integrated methods in classification accuracy.