A High-Dimensional and Multi-granularity Feature Selection Method Based on CNN and RF

Abstract

Feature engineering determines the upper limit of the performance of machine learning algorithm. And feature selection is the most critical step in feature engineering. However, the dimensional disasters are caused by high-dimensional and multi-granularity feature data, which makes effective feature selection very difficult. We propose a feature selection based on the Convolutional Neural Networks and Random Forest (FSCNNRF) for this issue. The model includes two parts, Feature Selection Convolutional Neural Networks (FSCNN) and Random Forest (RF). It can select more effective feature set by using FSCNN for dimensionality reduction and RF for feature selection. Firstly, the high-dimensional and multi-granularity feature data are subjected to dimensionality reduction processing by FSCNN, so that each feature becomes a single granularity feature. Then the RF is used to select valid features. Experiments show that the model has better effect on feature selection on high-dimensional and multi-granularity dataset and improves the performance of machine learning algorithms.