.png){kind=icon}
返回
Sparse group LASSO based uncertain feature selection
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-013-0156-6.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Uncertain data management and mining is becoming a hot topic in recent years. However, little attention has been paid to uncertain feature selection so far. In this paper, we introduce the sparse group least absolution shrinkage and selection operator (LASSO) technique to construct a feature selection algorithm for uncertain data. Each uncertain feature is represented with a probability density function. We take each feature as a group of values. Through analysis of the current four sparse feature selection methods, LASSO, elastic net, group LASSO and sparse group LASSO, the sparse group LASSO is introduced to select feature selection from uncertain data. The proposed algorithm can select not only the features between groups, but also the sub-features in groups. As the trained weights of feature groups are sparse, the groups of features with weight zero are removed. Experiments on nine UCI datasets show that feature selection for uncertain data can reduce the number of features and sub-features at the same time. Moreover it can produce comparable accuracy with all features.
Keyword:
Uncertain data
Feature selection
Sparse group LASSO
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
Purification of human galectin-1 produced in high-cell density cultures of recombinant Escherichia coli: a comparison with classic shake flask cultivation重组大肠杆菌高密度培养生产的人源凝集素-1纯化：与经典摇瓶培养的比较
PREAI
分享收藏
分享收藏A Comprehensive Comparison of a Lead-Acid Battery Electro-Thermal Performance Considering Different Charging Profiles.png)
2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON)
2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON)IF0
PREAI
分享收藏
查看更多内容