NDLI: Sparse group LASSO based uncertain feature selection

Content Provider	SpringerLink
Author	Xie, Zongxia Xu, Yong
Copyright Year	2013
Abstract	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.
Starting Page	201
Ending Page	210
Page Count	10
File Format	PDF
ISSN	18688071
Journal	International Journal of Machine Learning and Cybernetics
Volume Number	5
Issue Number	2
e-ISSN	1868808X
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-03-10
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Uncertain data Feature selection Sparse group LASSO Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Statistical Physics, Dynamical Systems and Complexity Systems Biology Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition Software

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