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A Computationally Inexpensive Classifier Merging Cellular Automata and MCP-Neurons
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
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Number of Authors: 5
2016 (English)In: Ubiquitous Computing and Ambient Intelligence: 10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016, Part II / [ed] Carmelo R. García, Pino Caballero-Gil, Mike Burmester, Alexis Quesada-Arencibia, Springer, 2016, Vol. 2, 368-379 p.Conference paper (Refereed)
Abstract [en]

There is an increasing need for personalised and context-aware services in our everyday lives and we rely on mobile and wearable devices to provide such services. Context-aware applications often make use of machine-learning algorithms, but many of these are too complex or resource-consuming for implementation on some devices that are common in pervasive and mobile computing. The algorithm presented in this paper, named CAMP, has been developed to obtain a classifier that is suitable for resource-constrained devices such as FPGA:s, ASIC:s or microcontrollers. The algorithm uses a combination of the McCulloch-Pitts neuron model and Cellular Automata in order to produce a computationally inexpensive classifier with a small memory footprint. The algorithm consists of a sparse binary neural network where neurons are updated using a Cellular Automata rule as the activation function. Output of the classifier is depending on the selected rule and the interconnections between the neurons. Since solving the input-output mapping mathematically can not be performed using traditional optimization algorithms, the classifier is trained using a genetic algorithm. The results of the study show that CAMP, despite its minimalistic structure, has a comparable accuracy to that of more advanced algorithms for the datasets tested containing few classes, while performing poorly on the datasets with a higher amount of classes. CAMP could thus be a viable choice for solving classification problems in environments with extreme demands on low resource consumption

Place, publisher, year, edition, pages
Springer, 2016. Vol. 2, 368-379 p.
Series
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 10070
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-60640DOI: 10.1007/978-3-319-48799-1_42ISBN: 978-3-319-48798-4ISBN: 978-3-319-48799-1OAI: oai:DiVA.org:ltu-60640DiVA: diva2:1049012
Conference
10th International Conference, UCAmI 2016, San Bartolomé de Tirajana, Gran Canaria, Spain, November 29 – December 2, 2016
Available from: 2016-11-23 Created: 2016-11-23 Last updated: 2016-11-23Bibliographically approved

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