Search Results for author:
Found 8 papers, 1 papers with code
DNN2LR: Automatic Feature Crossing for Credit Scoring
Accordingly, we can design an automatic feature crossing method to find feature interactions in DNN, and use them as cross features in LR.
DNN2LR: Interpretation-inspired Feature Crossing for Real-world Tabular Data
Simple classifiers, e. g., Logistic Regression (LR), are globally interpretable, but not powerful enough to model complex nonlinear interactions among features in tabular data.
Simplification of Graph Convolutional Networks: A Matrix Factorization-based Perspective
Moreover, we have also conducted experiments on a typical task of graph embedding, i. e., community detection, and the proposed UCMF model outperforms several representative graph embedding models.
An Empirical Study on Feature Discretization
When dealing with continuous numeric features, we usually adopt feature discretization.
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions
IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields.
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions
IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields.
Unifying Graph Convolutional Networks as Matrix Factorization
In recent years, substantial progress has been made on graph convolutional networks (GCN).
Automatically Learning Feature Crossing from Model Interpretation for Tabular Data
Automatically feature generation is a major topic of automated machine learning.
