scholarly journals SML-Bench – A benchmarking framework for structured machine learning

Semantic Web ◽  
2019 ◽  
Vol 10 (2) ◽  
pp. 231-245 ◽  
Author(s):  
Patrick Westphal ◽  
Lorenz Bühmann ◽  
Simon Bin ◽  
Hajira Jabeen ◽  
Jens Lehmann
Keyword(s):  
Machine Learning ◽  
Structured Machine Learning

Deep-Structured Machine Learning Model for the Recognition of Mixed-Defect Patterns in Semiconductor Fabrication Processes

2018 ◽  
Vol 31 (2) ◽  
pp. 315-322 ◽  
Author(s):  
Ghalia Tello ◽  
Omar Y. Al-Jarrah ◽  
Paul D. Yoo ◽  
Yousof Al-Hammadi ◽  
Sami Muhaidat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Model ◽  
Machine Learning Model ◽  
Semiconductor Fabrication ◽  
Structured Machine Learning

scholarly journals Identify Risk Pattern of E-Bike Riders in China Based on Machine Learning Framework

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1084 ◽  
Author(s):  
Wang ◽  
Kou ◽  
Song
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Police Department ◽  
Machine Learning Techniques ◽  
Crash Risk ◽  
Learning Framework ◽  
Safety Improvement ◽  
Electric Bicycle ◽  
Structured Machine Learning ◽  
Risk Pattern

In this paper, the risk pattern of e-bike riders in China was examined, based on tree-structured machine learning techniques. Three-year crash/violation data were acquired from the Kunshan traffic police department, China. Firstly, high-risk (HR) electric bicycle (e-bike) riders were defined as those with at-fault crash involvement, while others (i.e. non-at-fault or without crash involvement) were considered as non-high-risk (NHR) riders, based on quasi-induced exposure theory. Then, for e-bike riders, their demographics and previous violation-related features were developed based on the crash/violation records. After that, a systematic machine learning (ML) framework was proposed so as to capture the complex risk patterns of those e-bike riders. An ensemble sampling method was selected to deal with the imbalanced datasets. Four tree-structured machine learning methods were compared, and a gradient boost decision tree (GBDT) appeared to be the best. The feature importance and partial dependence were further examined. Interesting findings include the following: (1) tree-structured ML models are able to capture complex risk patterns and interpret them properly; (2) spatial-temporal violation features were found as important indicators of high-risk e-bike riders; and (3) violation behavior features appeared to be more effective than violation punishment-related features, in terms of identifying high-risk e-bike riders. In general, the proposed ML framework is able to identify the complex crash risk pattern of e-bike riders. This paper provides useful insights for policy-makers and traffic practitioners regarding e-bike safety improvement in China.

scholarly journals Structured machine learning tools for modelling characteristics of guided waves

2021 ◽  
Vol 156 ◽  
pp. 107628
Author(s):  
Marcus Haywood-Alexander ◽  
Nikolaos Dervilis ◽  
Keith Worden ◽  
Elizabeth J. Cross ◽  
Robin S. Mills ◽  
...  
Keyword(s):  
Machine Learning ◽  
Guided Waves ◽  
Learning Tools ◽  
Structured Machine Learning

scholarly journals Predicting gait events from tibial acceleration in rearfoot running: A structured machine learning approach

2021 ◽  
Vol 84 ◽  
pp. 87-92
Author(s):  
Pieter Robberechts ◽  
Rud Derie ◽  
Pieter Van den Berghe ◽  
Joeri Gerlo ◽  
Dirk De Clercq ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Tibial Acceleration ◽  
Structured Machine Learning

scholarly journals Structured machine learning: the next ten years

2008 ◽  
Vol 73 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Thomas G. Dietterich ◽  
Pedro Domingos ◽  
Lise Getoor ◽  
Stephen Muggleton ◽  
Prasad Tadepalli
Keyword(s):  
Machine Learning ◽  
Structured Machine Learning

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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