Semantic Diagnostics of Smart Factories

Savković, Ognjen; Kharlamov, Evgeny; Ringsquandl, Martin; Xiao, Guohui; Mehdi, Gulnar; Kalayc, Elem Güzel; Nutt, Werner; Horrocks, Ian

doi:10.1007/978-3-030-04284-4_19

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…Data stream analysis is gaining more and more attention thanks to the daily usage of a huge number of heterogeneous sources of streaming data such as sensors, wearables, smartphones and all other smart devices of the Internet of Things [17] in different application domains, including measurements in network monitoring and traffic management [6], log records and clickstreams in web surfing [2] as well as in virtual learning environments [12], manufacturing processes [19], and continuous monitoring of twitter posts [4]. Moreover, data stream analysis is part of the Big Data and Analytics enabling technology for Industry 4.0 [13].…”

Section: Introductionmentioning

confidence: 99%

Incremental Learning of Fuzzy Decision Trees for Streaming Data Classification

Pecori

Ducange

Marcelloni

2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Data stream analysis is growing in popularity in the last years since several application domains require to continuously and quickly analyse data produced by sensors with the aim of, for instance, reacting immediately when problems arise, or detecting new trends. The specificity of these domains imposes strict temporal constraints on machine learning algorithms to be used for mining useful insights. The Hoeffding Decision Tree (HDT) is a well-known classification algorithm for efficient streaming data classification. In this paper, with the aim of improving HDT accuracy and capability of handling noisy data, we exploit the learning procedure proposed in HDT for adapting a recently proposed fuzzy decision tree to cope with streaming data classification problems. We tested the fuzzy approach on a benchmark dataset for the on-line learning of data stream classification models. Results show that, during the on-line learning process, the fuzzy approach outperforms HDT in terms of accuracy.

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