Search Results for author:
Found 6 papers, 2 papers with code
Fast Genetic Algorithm for feature selection -- A qualitative approximation approach
In this paper, we propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets.
Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks
This paper investigates how various randomization techniques impact Deep Neural Networks (DNNs).
Beyond Random Noise: Insights on Anonymization Strategies from a Latent Bandit Study
This paper investigates the issue of privacy in a learning scenario where users share knowledge for a recommendation task.
Dynamic Causal Explanation Based Diffusion-Variational Graph Neural Network for Spatio-temporal Forecasting
Also, by F1-score and probability distribution analysis, we demonstrate that DVGNN better reflects the causal relationship and uncertainty of dynamic graphs.
Surrogate-Assisted Genetic Algorithm for Wrapper Feature Selection
Such a framework allows for using wrapper approaches in a much more computationally efficient way, significantly increasing the quality of feature selection solutions achievable, especially on large datasets.
Transfer learning for Remaining Useful Life Prediction Based on Consensus Self-Organizing Models
In this work, we present a TL method for predicting Remaining Useful Life (RUL) of equipment, under the assumption that labels are available only for the source domain and not the target domain.
