Paper Info
Title
A Collaborative Deep And Shallow Semisupervised Learning Framework For Mobile App Classification
Abstract
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.
Year
DOI
Venue
2020
10.1155/2020/4521723
MOBILE INFORMATION SYSTEMS
Field
DocType
Volume
Mobile app,Open problem,Computer science,Textual information,Exploit,Artificial intelligence,Deep learning,Classifier (linguistics),Mobile apps,Machine learning,Distributed computing
Journal
2020
ISSN
Citations 
PageRank 
1574-017X
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Mingqi Lv122612.61
Chao Huang200.34
Tieming Chen3295.11
Ting Wang4369.43