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Using Machine Learning on Sensor Data
Journal Title CIT. Journal of Computing and Information Technology
Journal Abbreviation CIT
Publisher Group University of Zagreb
Website http://cit.srce.unizg.hr/index.php/CIT
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Title Using Machine Learning on Sensor Data
Authors Moraru, Alexandra; Pesko, Marko; Porcius, Maria; Fortuna, Carolina; Mladenic, Dunja
Abstract Extracting useful information from raw sensor data requires specific methods and algorithms. We describe a vertical system integration of a sensor node and a toolkit of machine learning algorithms for predicting the number of persons located in a closed space. The dataset used as input for the learning algorithms is composed from automatically collected sensor data and additional manually introduced data. We analyze the dataset and evaluate the performance of two types of machine learning algorithms on this dataset: classification and regression. With our system settings, the experiments show that augmenting sensor data with proper information can improve prediction results and also the classification algorithm performed better.
Publisher University of Zagreb, University Computing Centre - SRCE
Date 2011-02-04
Source Journal of Computing and Information Technology Vol 18, No 4 (2010): Special Issue from the 2010 ITI Conference
Rights CIT. Journal of Computing and Information Technology is an open access journal.Authors who publish with this journal agree to the following terms:Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work´s authorship and initial publication in this journal.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal´s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

 

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