Paper Info
Title
Online Feature Selection Based on Passive-Aggressive Algorithm with Retaining Features.
Abstract
Feature selection is an important topic in data mining and machine learning, and has been extensively studied in many literature. Unlike traditional batch learning methods, online learning is more efficient for real-world applications. Most existing studies of online learning require accessing all the features of training instances, but in real world, it is often expensive to acquire the full set of attributes. In online feature selection process, when a training instance arrive, a fixed small number of features will be selected, and then the other features will be ignored. However, those ignored features may be useful and selected in later instances. If we only consider the new instances for these special features, it will lead to extreme errors. To address these issues, we improved a novel algorithm with Passive-Aggressive Algorithm and retaining features. Then we evaluate the performance of the proposed algorithms for online feature selection on several public datasets, and we can see from the experiments that our algorithm consistently surpassed the baseline algorithms for all the situations.
Year
DOI
Venue
2015
10.1007/978-3-319-25255-1_58
APWeb
Keywords
Field
DocType
Feature Selection,Online Learning,Binary Classification
Small number,Online learning,Data mining,Feature selection,Binary classification,Computer science,Feature (computer vision),Algorithm,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
9313
0302-9743
2
PageRank 
References 
Authors
0.37
18
2
Name
Order
Citations
PageRank
Zheng Hai-Tao114224.39
Zheng Hai-Tao214224.39