Fuzzy feature factorization machine: Bridging feature interaction, selection, and construction

Feature selection is an effective data pre-processing technique that aims to select useful features. This technique has been widely applied in machine learning and data mining to improve the performance of learning models in downstream tasks. However, traditional feature selection approaches face two issues: (1) ignoring the essential interaction between features, and (2) sacrificing the high-level information hidden in features. Thus, the fuzzy technique is applied to frame a unified architecture named fuzzy feature factorization machine (F3M). Essentially, F3M leverages a scheme of pairwise feature combination that serves as a basis for bridging feature interaction, selection and feature construction. Specifically, pairwise feature combination is used to exploit feature interaction for fuzzy criteria-based feature selection and induce fuzzy feature interaction information for high-level feature construction. It is worth noting that F3M is a general framework that allows the use of any feature selection procedure. The superiority of F3M is validated using 17 public datasets and 5 schizophrenia datasets. The experimental results demonstrate that F3M can identify features with better classification performance, can be effectively applied to noisy data, and can also contribute to the prediction of schizophrenia incidence.