Abstract
Due to the proliferation of mobile computing and Internet of Things devices, there is an urgent need to push the machine learning frontiers to the network edge so as to fully unleash the potential of the edge big data. Since feature selection becomes a fundamental step in the data analysis process, the need to perform this preprocessing task in a reduced precision environment arises as well. To achieve this, limited bit depth conditioned mutual information is proposed within a Markov Blanket procedure. This work also shows the process of generating approximate tables and obtaining the values required to test the independence of the variables involved in the algorithm. Finally, it compares the results obtained during the whole process, from preprocessing to classification, using different numbers of bits.
This research has been financially supported in part by the Spanish Ministerio de Economía y Competitividad (research project TIN2015-65069-C2-1-R), by European Union FEDER funds and by the Consellería de Industria of the Xunta de Galicia (research project GRC2014 /035). CITIC as a Research Centre of the Galician University System is financed by the Conselleria de Education, Universidades e Formación Profesinal (Xunta de Galicia) through the ERDF (80%), Operational Programme ERDF Galicia 2014–2020 and the remaining 20% by the Secretaria Xeral de Universidades (Ref. ED431G 2019/01). Project supported by a 2018 Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation.
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Morán-Fernández, L., Blanco-Mallo, E., Sechidis, K., Alonso-Betanzos, A., Bolón-Canedo, V. (2020). When Size Matters: Markov Blanket with Limited Bit Depth Conditional Mutual Information. In: Gama, J., et al. IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning. ITEM IoT Streams 2020 2020. Communications in Computer and Information Science, vol 1325. Springer, Cham. https://doi.org/10.1007/978-3-030-66770-2_18
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DOI: https://doi.org/10.1007/978-3-030-66770-2_18
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