Scalable self-taught deep-embedded learning framework for drug abuse spatial behaviors detection

Wuji Liu, Xinyue Ye, Hai Phan, Han Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Drug abuse has become an increasingly challenging issue national wide in the United States, while each state has its own legislation regarding such behavior which further stimulates different semantic representations of such behavior over space. To build an accurate and robust classifier to detect such behaviors with spatial variance remains challenging due to the existence of large noise in tweets and limited number of labeled data. Most efforts have utilized humans to label tweets for the base classifier training. The randomness of human labeled data would limit the generalization of base model trained. We propose a deep learning-based centroid-attention framework to consider the spatial variance. We further explore the effect of state-based exemplars on the base model. The performance of the base classifier is thus enhanced.

Original languageEnglish (US)
Title of host publicationComputational Data and Social Networks - 8th International Conference, CSoNet 2019, Proceedings
EditorsAndrea Tagarelli, Hanghang Tong
PublisherSpringer
Pages223-228
Number of pages6
ISBN (Print)9783030349790
DOIs
StatePublished - 2019
Event8th International Conference on Computational Data and Social Networks, CSoNet 2019 - Ho Chi Minh City, Viet Nam
Duration: Nov 18 2019Nov 20 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11917 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Computational Data and Social Networks, CSoNet 2019
CountryViet Nam
CityHo Chi Minh City
Period11/18/1911/20/19

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Keywords

  • Deep learning
  • Drug abuse
  • Spatial effects

Fingerprint Dive into the research topics of 'Scalable self-taught deep-embedded learning framework for drug abuse spatial behaviors detection'. Together they form a unique fingerprint.

Cite this