.png){kind=icon}
返回
Discrimination Neural Network Model for Binary Classification Tasks on Tabular Data
.png){kind=icon}
OA
AI
DOI：10.1109/ACCESS.2023.3243919.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
For the classification task, neural network-based approaches attempt to distinguish between two distributions by determining the joint distribution of input variables for each class. However, the most challenging task is still to classify the observations in the overlapping region of two classes. In this work, we propose a new discrimination neural network (DiscNN) architecture to address this issue. Our DiscNN learns to embed the initial input into more informative representations with better discriminability between the two distributions based on the cosine embedding loss. We also train our proposed model using the few-shot learning method to extract better-generalized representations from the initial input. We applied the DiscNN model to 35 tabular datasets from the OpenML-CC18 benchmark for a binary classification task. Our model showed superior performances on 28 datasets of them. In addition, we also performed experiments on 95 imbalanced datasets from the KEEL repository. The experiment results showed that the DiscNN outperformed the state-of-the-art models, including CatBoost, LightGBM, TabNet, VIME and Scarf, by around 0.23% AUC, 0.20% G-mean, and 1.06% F1 score.
Keyword:
Data models
Task analysis
Predictive models
Neural networks
Training
Loss measurement
Prediction algorithms
Neural network
cosine similarity
classification task
imbalanced problem
tabular data
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
机构
引用论文
分享收藏Neural Network-Augmented Locally Adaptive Linear Regression Model for Tabular Data表格数据的神经网络增强局部自适应线性回归模型.png)
SUSTAINABILITYOAAI
SUSTAINABILITYIF3.3
OAAI分享收藏
THE MEANING AND USE OF THE AREA UNDER A RECEIVER OPERATING CHARACTERISTIC (ROC) CURVE受试者工作特征 (ROC) 曲线下面积的含义和用途RADIOLOGY
RADIOLOGYIF15.2
PREAI
分享收藏GEV-NN: A deep neural network architecture for class imbalance problem in binary classification
PREAI
分享收藏Analysis and assessment of ship collision accidents using Fault Tree and Multiple Correspondence Analysis基于故障树和多重对应分析的船舶碰撞事故分析与评估
PREAI
分享收藏
查看更多内容