Feature Selection Using Stochastic Gates 1. 理解“随机门(Stochastic Gates)”在特征选择中的应用 随机门(Stochastic Gates)是一种用于特征选择的方法,特别是在高维非线性函数估计问题中。它通过概率松弛的方式近似l0范数,从而选择出对预测任务最相关的特征子集。这种方法结合了神经网络的学习能力
Feature selection problems have been extensively studied in the setting of linear estimation (e.g. LASSO), but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in neural network estimation problems. The new ...
Feature Selection using Stochastic Gates (STG) is a method for feature selection in neural network estimation problems. The new procedure is based on probabilistic relaxation of the l0 norm of features, or the count of the number of selected features. The proposed framework simultaneously learns ei...
Moving forward to recently proposed techniques, Thakkar and Lohiya proposed a wrapper-filter hybrid feature selection methodology for Deep Neural Network-based Intrusion Detection Systems [39]. In this approach, the features are ranked utilizing a fusion of statistical importance using standard deviation ...
Some well-known feature selection algorithms using the filter approach are filter-based ant colony optimization [15], filter-based binary particle swarm optimization [16,17], and relevance-redundancy feature selection based on ant colony optimization (RRFSACO) [18]. The wrapper approach utilizes a ...
Feature selection using RFEMLP Sequence-based feature extraction CodeBERT The pre-trained models are effective in vulnerability prediction108,109. The CodeBERT combines bidirectional encoder representation from transformers and optimized BERT called RoBERTa110. The BERT is a self-supervised model that utilize...
Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning 2024, Energy Citation Excerpt : From the data-centric perspective, although the effectiveness of DANN has been studied by post-hoc analysis (source building selection, the...
Hemolytic peptides are therapeutic peptides that damage red blood cells. However, therapeutic peptides used in medical treatment must exhibit low toxicity to red blood cells to achieve the desired therapeutic effect. Therefore, accurate prediction of the
Feature subset selection is essential for identifying relevant and non-redundant features, which enhances classification accuracy and simplifies machine le
Abstract Relative humidity (RH) is one of the important processes in the hydrology cycle which is highly stochastic. Accurate RH prediction can be highly beneficial for several water resources engineering practices. In this study, extreme gradient boosting (XGBoost) approach “as a selective input pa...