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Automated Machine Learning in Practice: State of the Art and Recent Results

Lukas Tuggener, Mohammadreza Amirian, Katharina Rombach, Stefan Lorwald, Anastasia Varlet, Christian Westermann, Thilo Stadelmann
2019 2019 6th Swiss Conference on Data Science (SDS)  
This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results on the most important AutoML algorithms  ...  Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so.  ...  ACKNOWLEDGEMENT We are grateful for support by Innosuisse grant 25948.1 PFES "Ada" and helpful discussions with Martin Jaggi.  ... 
doi:10.1109/sds.2019.00-11 dblp:conf/sds2/TuggenerARLVWS19 fatcat:okiclde7nrb3xgyatmlqjh4use

Machine Learning in Automated Asset Management Processes 4.1

Marcus Becker, Mikhail Beketov, Manuel Wittke
2021 Die Unternehmung. Swiss Journal of Business Research and Practice  
With an increasing amount of publicly available financial data, the foundation for applying machine learning (ML) algorithms has been paved.  ...  We examine the question in which process steps of automated investment advice ML algorithms could be applied and investigate which implementations have already been placed on the market.  ...  Methods of Machine Learning-Algorithms suitable to Automated Asset Management 4.1 Machine Learning (ML) describes a set of methods that can automatically detect patterns in data, and then use the uncovered  ... 
doi:10.5771/0042-059x-2021-3-411 fatcat:ww5vahreurc3vdscdcx3h6m2vm

Research on automatic irrigation control: State of the art and recent results

R. Romero, J.L. Muriel, I. García, D. Muñoz de la Peña
2012 Agricultural Water Management  
In particular, we present some promising preliminary experimental results of four different control strategies applied to fruit trees in southern Spain to show the potential of the application of control  ...  The aforementioned issues justify the need for a sustainable and rational use of water in irrigated crops, which motivates the implementation of new precise automatic irrigation technologies based on control  ...  Recent results Our group has been working over the last 4 years in developing and testing new irrigation control strategies applied to fruit orchards growing in the south-west of Spain.  ... 
doi:10.1016/j.agwat.2012.06.026 fatcat:dptntdzvhbgjppimum3hbsxxky

Automating In-Network Machine Learning [article]

Changgang Zheng, Mingyuan Zang, Xinpeng Hong, Riyad Bensoussane, Shay Vargaftik, Yaniv Ben-Itzhak, Noa Zilberman
2022 arXiv   pre-print
Using programmable network devices to aid in-network machine learning has been the focus of significant research.  ...  The results show that Planter-based in-network machine learning algorithms can run at line rate, have a negligible effect on latency, coexist with standard switching functionality, and have no or minor  ...  Figure 18 : 18 Relative accuracy between switch and scikit-learn results with two typical model hyperparameters based on CICIDS dataset. Table 1 : 1 State-of-the-art in-network ML solutions.  ... 
arXiv:2205.08824v1 fatcat:lphkldydordxvce2p5wvgvskca

Toward systematic review automation: a practical guide to using machine learning tools in research synthesis

Iain J. Marshall, Byron C. Wallace
2019 Systematic Reviews  
In this practical guide, we provide an overview of current machine learning methods that have been proposed to expedite evidence synthesis.  ...  Technologies and methods to speed up the production of systematic reviews by reducing the manual labour involved have recently emerged.  ...  State-of-the-art methods for both text classification and data extraction use machine learning (ML) techniques, rather than, e.g. rule-based methods.  ... 
doi:10.1186/s13643-019-1074-9 pmid:31296265 pmcid:PMC6621996 fatcat:qbhycs7mdvc5hjewzz3pv27yeq

Machine Learning in Automated Text Categorization [article]

Fabrizio Sebastiani
2001 arXiv   pre-print
In the research community the dominant approach to this problem is based on machine learning techniques: a general inductive process automatically builds a classifier by learning, from a set of preclassified  ...  The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last ten years, due to the increased availability of documents in digital form  ...  Acknowledgements This paper owes a lot to the suggestions and constructive criticism of Norbert Fuhr and David Lewis.  ... 
arXiv:cs/0110053v1 fatcat:fmqocqebfnahlhbnpizcvo6kri

Automated Data Validation in Machine Learning Systems

Felix Biessmann, Jacek Golebiowski, Tammo Rukat, Dustin Lange, Philipp Schmidt
2021 IEEE Data Engineering Bulletin  
Machine Learning (ML) algorithms are a standard component of modern software systems.  ...  Machine Learning (ML) technology has become a standard component in modern software systems.  ...  In the following we will highlight some of the state of the art solutions to automated data validation in the context of ML systems.  ... 
dblp:journals/debu/BiessmannGRL021 fatcat:x3fugemqhjgsramr26th3eu2oe

Machine Learning for Automated Theorem Proving: Learning to Solve SAT and QSAT

Sean B. Holden
2021 Foundations and Trends® in Machine Learning  
These techniques have largely exploited algorithmic developments; however machine learning also exerts a significant influence in the development of state-ofthe-art solvers.  ...  Here, the application of machine learning is delicate, as in many cases, even if a relevant learning problem can be solved, it may be that incorporating the result into a SAT or QSAT solver is counterproductive  ...  It is equally my hope that ATP researchers will gain a complementary understanding, giving them a clear appreciation of how state-of-the-art machine learning might help them to design better solvers.  ... 
doi:10.1561/2200000081 fatcat:nanrmz5idjeprmccdysdlvrt3a

Automated doubt identification from informal reflections through hybrid sentic patterns and machine learning approach

Siaw Ling Lo, Kar Way Tan, Eng Lieh Ouh
2021 Research and Practice in Technology Enhanced Learning  
In this paper, we derived a hybrid approach that leverages a novel Doubt Sentic Pattern Detection (SPD) algorithm and a machine learning model to automate the identification of doubts from students' informal  ...  Using reflections as a feedback mechanism and automated doubt detection can pave the way to a promising approach for learner-centered teaching and personalized learning.  ...  Research and Practice in Technology Enhanced Learning (2021) 16:1 Page 23 of 24 Received: 3 July 2020 Accepted: 28 January 2021  ... 
doi:10.1186/s41039-021-00149-9 fatcat:vxnuz3zdpbdyxmszyiqxgmuqqi

Interpretable Automated Machine Learning in Maana(TM) Knowledge Platform [article]

Alexander Elkholy, Fangkai Yang, Steven Gustafson
2019 arXiv   pre-print
validate machine learning models, in the sense that the insight of developing machine learning solutions are not structurally encoded, justified and transferred.  ...  In this paper we describe Maana Meta-learning Service, an interpretable and interactive automated machine learning service residing in Maana Knowledge Platform that performs machine-guided, user assisted  ...  Unfortunately, this level of understanding remains somewhat of a "dark art" in that the knowledge and judgment used to find good domain-specific machine learning pipelines is usually found in the heads  ... 
arXiv:1905.02168v1 fatcat:5kt4cdgya5c4dev65v34bolwgi

Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach [article]

Sen Chen, Minhui Xue, Lingling Fan, Shuang Hao, Lihua Xu, Haojin Zhu, Bo Li
2017 arXiv   pre-print
In this paper, we explore the feasibility of constructing crafted malware samples; examine how machine-learning classifiers can be misled under three different threat models; then conclude that injecting  ...  Today, sophisticated attackers can adapt by maximally sabotaging machine-learning classifiers via polluting training data, rendering most recent machine learning-based malware detection tools (such as  ...  We reviewed several challenges for the malware detection problem. We showed how the conventional machine learning classifiers can fail against determined attackers.  ... 
arXiv:1706.04146v3 fatcat:f7yzifuahff6dfyaihlrnn3gfa

A Machine Learning Approach for Automated Filling of Data Entry Forms [article]

Hichem Belgacem, Xiaochen Li, Domenico Bianculli, Lionel C. Briand
2022 arXiv   pre-print
In this paper, we propose LAFF, a learning-based automated approach for filling categorical fields in data entry forms.  ...  To improve its learning ability, LAFF uses local modeling to effectively mine the local dependencies of fields in a cluster of input instances.  ...  ACKNOWLEDGMENTS The authors would like to thank the anonymous referees for their valuable comments and helpful suggestions.  ... 
arXiv:2202.08572v1 fatcat:ixloe4bse5hdnh6i5ehw7ltgj4

A Review of Machine Learning Techniques in Analog Integrated Circuit Design Automation

Rayan Mina, Chadi Jabbour, George E. Sakr
2022 Electronics  
The objectives of this paper were: (1) to provide a comprehensive overview of the existing state-of-the-art machine learning techniques used in analog circuit sizing and analyze their effectiveness in  ...  Finally, the different analog circuits on which machine learning techniques were applied are also presented and their results discussed from a circuit designer perspective.  ...  Acknowledgments: The authors would like to thank Marc Ibrahim from Saint-Joseph University of Beirut for his insightful comments on the efficiency of global optimization methods and evolutionary algorithms  ... 
doi:10.3390/electronics11030435 fatcat:pnpblgcserebtmkm4i2fkgm54i

Self-Service Data Science in Healthcare with Automated Machine Learning

Richard Ooms, Marco Spruit
2020 Applied Sciences  
(1) Background: This work investigates whether and how researcher-physicians can be supported in their knowledge discovery process by employing Automated Machine Learning (AutoML). (2) Methods: We take  ...  and comparing results.  ...  Automation of the knowledge discovery process can increase the adoption of analytics by enabling domain experts to contribute to the knowledge discovery in the field using state-of the art techniques in  ... 
doi:10.3390/app10092992 fatcat:blp4a576hnemtn3to7bf4aa2me

The responsible development of Automated Machine Learning technology in REDD+

David Van Der Zande, Dr. Sanneke Kloppenburg
2021 Zenodo  
The expert interviews were conducted with experts in machine learning and REDD+.  ...  With the use of value-sensitive design and responsible innovation software development methods, the development and potential use of machine learning for the use of REDD+ MRV processes was analysed with  ...  AutoML in forest monitoring As a new and only recently (in the past ten years) developed technology, Automated Machine Learning data analysis methods are not yet used in the monitoring tools of REDD+.  ... 
doi:10.5281/zenodo.5902770 fatcat:pitr6rzm75a6vizk6jvbderf6i
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