CBARS

Cluster based classification for activity recognition systems

Zahraa Said Abdallah, Mohamed Medhat Gaber, Bala Srinivasan, Shonali Krishnaswamy

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    11 Citations (Scopus)

    Abstract

    Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel Cluster Based Classification for Activity Recognition Systems, CBARS. The novel approach processes activities as clusters to build a robust classification framework. CBARS integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition dataset have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.
    Original languageEnglish
    Title of host publicationAdvanced Machine Learning Technologies and Applications
    Subtitle of host publicationFirst International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012, Proceedings
    EditorsAboul Ella Hassanien, Abdel-Badeeh M. Salem, Rabie Ramadan, Tai-hoon Kim
    Place of PublicationHeidelberg [Germany]
    PublisherSpringer
    Pages82 - 91
    Number of pages10
    Volume322
    ISBN (Electronic)9783642353260
    ISBN (Print)9783642353253
    DOIs
    Publication statusPublished - 2012
    EventInternational Conference on Advanced Machine Learning Technologies and Applications (AMLTA) 2012 - Cairo, Egypt
    Duration: 8 Dec 201210 Dec 2012
    Conference number: 1st
    https://link.springer.com/book/10.1007/978-3-642-35326-0 (Conference Proceedings)

    Publication series

    NameCommunications in Computer and Information Science
    PublisherSpringer
    Volume322
    ISSN (Print)1865-0929
    ISSN (Electronic)1865-0937

    Conference

    ConferenceInternational Conference on Advanced Machine Learning Technologies and Applications (AMLTA) 2012
    Abbreviated titleAMLTA 2012
    CountryEgypt
    CityCairo
    Period8/12/1210/12/12
    Internet address

    Keywords

    • Activity recognition
    • Cluster based classification
    • Hybrid similarity measure

    Cite this

    Abdallah, Z. S., Gaber, M. M., Srinivasan, B., & Krishnaswamy, S. (2012). CBARS: Cluster based classification for activity recognition systems. In A. E. Hassanien, A-B. M. Salem, R. Ramadan, & T. Kim (Eds.), Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012, Proceedings (Vol. 322, pp. 82 - 91). (Communications in Computer and Information Science; Vol. 322). Heidelberg [Germany]: Springer. https://doi.org/10.1007/978-3-642-35326-0
    Abdallah, Zahraa Said ; Gaber, Mohamed Medhat ; Srinivasan, Bala ; Krishnaswamy, Shonali. / CBARS : Cluster based classification for activity recognition systems. Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012, Proceedings. editor / Aboul Ella Hassanien ; Abdel-Badeeh M. Salem ; Rabie Ramadan ; Tai-hoon Kim. Vol. 322 Heidelberg [Germany] : Springer, 2012. pp. 82 - 91 (Communications in Computer and Information Science).
    @inproceedings{9d81c3ffbe7f427fae63739cb5ed75a8,
    title = "CBARS: Cluster based classification for activity recognition systems",
    abstract = "Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel Cluster Based Classification for Activity Recognition Systems, CBARS. The novel approach processes activities as clusters to build a robust classification framework. CBARS integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition dataset have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.",
    keywords = "Activity recognition, Cluster based classification, Hybrid similarity measure",
    author = "Abdallah, {Zahraa Said} and Gaber, {Mohamed Medhat} and Bala Srinivasan and Shonali Krishnaswamy",
    year = "2012",
    doi = "10.1007/978-3-642-35326-0",
    language = "English",
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    Abdallah, ZS, Gaber, MM, Srinivasan, B & Krishnaswamy, S 2012, CBARS: Cluster based classification for activity recognition systems. in AE Hassanien, A-BM Salem, R Ramadan & T Kim (eds), Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012, Proceedings. vol. 322, Communications in Computer and Information Science, vol. 322, Springer, Heidelberg [Germany], pp. 82 - 91, International Conference on Advanced Machine Learning Technologies and Applications (AMLTA) 2012, Cairo, Egypt, 8/12/12. https://doi.org/10.1007/978-3-642-35326-0

    CBARS : Cluster based classification for activity recognition systems. / Abdallah, Zahraa Said; Gaber, Mohamed Medhat; Srinivasan, Bala; Krishnaswamy, Shonali.

    Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012, Proceedings. ed. / Aboul Ella Hassanien; Abdel-Badeeh M. Salem; Rabie Ramadan; Tai-hoon Kim. Vol. 322 Heidelberg [Germany] : Springer, 2012. p. 82 - 91 (Communications in Computer and Information Science; Vol. 322).

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    TY - GEN

    T1 - CBARS

    T2 - Cluster based classification for activity recognition systems

    AU - Abdallah, Zahraa Said

    AU - Gaber, Mohamed Medhat

    AU - Srinivasan, Bala

    AU - Krishnaswamy, Shonali

    PY - 2012

    Y1 - 2012

    N2 - Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel Cluster Based Classification for Activity Recognition Systems, CBARS. The novel approach processes activities as clusters to build a robust classification framework. CBARS integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition dataset have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.

    AB - Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel Cluster Based Classification for Activity Recognition Systems, CBARS. The novel approach processes activities as clusters to build a robust classification framework. CBARS integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition dataset have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.

    KW - Activity recognition

    KW - Cluster based classification

    KW - Hybrid similarity measure

    UR - http://www.scopus.com/inward/record.url?scp=84880355940&partnerID=8YFLogxK

    UR - https://link.springer.com/book/10.1007%2F978-3-642-35326-0

    U2 - 10.1007/978-3-642-35326-0

    DO - 10.1007/978-3-642-35326-0

    M3 - Conference Paper

    SN - 9783642353253

    VL - 322

    T3 - Communications in Computer and Information Science

    SP - 82

    EP - 91

    BT - Advanced Machine Learning Technologies and Applications

    A2 - Hassanien, Aboul Ella

    A2 - Salem, Abdel-Badeeh M.

    A2 - Ramadan, Rabie

    A2 - Kim, Tai-hoon

    PB - Springer

    CY - Heidelberg [Germany]

    ER -

    Abdallah ZS, Gaber MM, Srinivasan B, Krishnaswamy S. CBARS: Cluster based classification for activity recognition systems. In Hassanien AE, Salem A-BM, Ramadan R, Kim T, editors, Advanced Machine Learning Technologies and Applications: First International Conference, AMLTA 2012, Cairo, Egypt, December 8-10, 2012, Proceedings. Vol. 322. Heidelberg [Germany]: Springer. 2012. p. 82 - 91. (Communications in Computer and Information Science). https://doi.org/10.1007/978-3-642-35326-0