A Collaborative Deep and Shallow Semisupervised Learning Framework for Mobile App Classification
Joint Authors
Chen, Tieming
Lv, MingQi
Huang, Chao
Wang, Ting
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-14
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Telecommunications Engineering
Abstract EN
With the rapid growth of mobile Apps, it is necessary to classify the mobile Apps into predefined categories.
However, there are two problems that make this task challenging.
First, the name of a mobile App is usually short and ambiguous to reflect its real semantic meaning.
Second, it is usually difficult to collect adequate labeled samples to train a good classifier when a customized taxonomy of mobile Apps is required.
For the first problem, we leverage Web knowledge to enrich the textual information of mobile Apps.
For the second problem, the mostly utilized approach is the semisupervised learning, which exploits unlabeled samples in a cotraining scheme.
However, how to enhance the diversity between base learners to maximize the power of the cotraining scheme is still an open problem.
Aiming at this problem, we exploit totally different machine learning paradigms (i.e., shallow learning and deep learning) to ensure a greater degree of diversity.
To this end, this paper proposes Co-DSL, a collaborative deep and shallow semisupervised learning framework, for mobile App classification using only a few labeled samples and a large number of unlabeled samples.
The experiment results demonstrate the effectiveness of Co-DSL, which could achieve over 85% classification accuracy by using only two labeled samples from each mobile App category.
American Psychological Association (APA)
Lv, MingQi& Huang, Chao& Chen, Tieming& Wang, Ting. 2020. A Collaborative Deep and Shallow Semisupervised Learning Framework for Mobile App Classification. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1192385
Modern Language Association (MLA)
Lv, MingQi…[et al.]. A Collaborative Deep and Shallow Semisupervised Learning Framework for Mobile App Classification. Mobile Information Systems No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1192385
American Medical Association (AMA)
Lv, MingQi& Huang, Chao& Chen, Tieming& Wang, Ting. A Collaborative Deep and Shallow Semisupervised Learning Framework for Mobile App Classification. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1192385
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192385