Feature Selection Algorithm Based on CFS Algorithm Emphasizing Data Discrimination

Abstract

In the training of the neural network model, the large number of features in the data set will lead to the complexity of the network model and high time cost. Therefore, the feature selection operation of the original data set is carried out to select the feature subset conducive to model training to improve the model's performance. The traditional feature selection algorithm has the problems of a thin process and needs help to eliminate the features with small discrimination. Therefore, this paper proposes the discrimination filtering formula and DI-CFS feature selection algorithm. The discrimination filtering formula can filter out invalid features and inefficient features with small discrimination. DI-CFS algorithm combines discrimination filtering formula, isolation forest algorithm, and improved CFS algorithm. On a set of wind turbine data, the DI-CFS algorithm and other traditional feature selection algorithms are used to select features from the data, respectively, and the obtained feature subsets are input into the same neural network model for training and performing classification tasks. The experimental results show that the discrimination filtering formula has a positive effect, and the DI-CFS algorithm has a better feature selection effect.

Letter2vec+Freq2vec: A Comprehensive Approach to Text Vectorization and Predicting Word Difficulty

“…Deep learning models like LSTM tend to overfit when data is insufficient. Instead, we use the Stacking approach9, integrating XGBoost [10], CatBoost [11], and LightBoost [12], to learn any form of function through sample data and handle nonlinear problems [9][10][11][12]. This approach effectively reduces errors and improves model robustness in small sample data sets, making it ideal for our problem.…”