Work

Using Machine Learning on Sensor Data

Year: 2010

Type: article

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.

Source: Journal of Computing and Information Technology

DOI: 10.2498/cit.1001913

Authors: Alexandra Moraru, Marko Pesko, Maria Porcius, Carolina Fortuna, Dunja Mladenic

Institutions: Jožef Stefan Institute, Jožef Stefan International Postgraduate School

Language: English

Topic: Indoor and Outdoor Localization Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $485

Funder: Javna Agencija za Raziskovalno Dejavnost RS