Learning from Life-Logging Data by Hybrid HMM: A Case Study on Active States Prediction

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Ni:2016:BioMed,

• author = "Ji Ni and Tryphon Lambrou and Xujiong Ye",
• title = "Learning from Life-Logging Data by Hybrid {HMM}: A Case Study on Active States Prediction",
• booktitle = "12th international Conference on Biomedical Engineering Biomedical Engineering (BioMed 2016)",
• year = "2016",
• editor = "Arnold Baca",
• address = "Innsbruck, Austria",
• month = feb # " 15-16",
• organisation = "IASTED",
• keywords = "genetic algorithms, genetic programming, SVM, ehealth, machine learning, wearable sensor, life-logging data",
• bibsource = "OAI-PMH server at eprints.lincoln.ac.uk",
• language = "en",
• oai = "oai:eprints.lincoln.ac.uk:23092",
• relation = "10.2316/P.2016.832-019",
• type = "Conference or Workshop contribution; NonPeerReviewed",
• URL = "http://eprints.lincoln.ac.uk/23092/",
• URL = "http://eprints.lincoln.ac.uk/23092/1/832-019.pdf",
• URL = "http://www.actapress.com/Abstract.aspx?paperId=456195",
• DOI = "doi:10.2316/P.2016.832-019",
• abstract = "In this paper, we have proposed employing a hybrid classifier-hidden Markov model (HMM) as a supervised learning approach to recognise daily active states from sequential life-logging data collected from wearable sensors. We generate synthetic data from real dataset to cope with noise and incompleteness for training purpose and, in conjunction with HMM, propose using a multiobjective genetic programming (MOGP) classifier in comparison of the support vector machine (SVM) with variant kernels. We demonstrate that the system with either algorithm works effectively to recognise personal active states regarding medical reference. We also illustrate that MOGP yields generally better results than SVM without requiring an ad hoc kernel.",

}

Genetic Programming entries for Ji Ni Tryphon Lambrou Xujiong Ye

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