Overview of Prominent Machine Learning and Data Mining Methods with Example Applications to the Medical Domain

2013 ◽  
pp. 47-68
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Mining Methods ◽  
Medical Domain

Big Data and Service Science

2016 ◽  
pp. 180-196
Author(s):  
Tu-Bao Ho ◽  
Siriwon Taewijit ◽  
Quang-Bach Ho ◽  
Hieu-Chi Dam
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Service Science ◽  
Customer Relationship ◽  
Service Value ◽  
Mining Methods ◽  
The Relationship

Big data is about handling huge and/or complex datasets that conventional technologies cannot handle or handle well. Big data is currently receiving tremendous attention from both industry and academia as there is much more data around us than ever before. This chapter addresses the relationship between big data and service science, especially how big data can contribute to the process of co-creation of service value. In particular, the value co-creation in terms of customer relationship management is mentioned. The chapter starts with brief descriptions of big data, machine learning and data mining methods, service science and its model of value co-creation, and then addresses the key idea of how big data can contribute to co-create service value.

scholarly journals Ensemble Data Mining Methods

2011 ◽  
pp. 448-453
Author(s):  
Nikunj C. Oza
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Accuracy ◽  
Ensemble Methods ◽  
Multiple Models ◽  
Machine Learning Methods ◽  
Ensemble Data ◽  
Basic Goal ◽  
Mining Methods ◽  
The Individual

Ensemble data mining methods, also known as committee methods or model combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: Each member of the committee should be as competent as possible, but the members should complement one another. If the members are not complementary, that is, if they always agree, then the committee is unnecessary — any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

scholarly journals Credit Card Fraud Detection Using Machine Learning Classification Algorithms over Highly Imbalanced Data

2020 ◽  
Vol 5 (3) ◽  
pp. 138-146 ◽  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Credit Card ◽  
Imbalanced Data ◽  
Learning Systems ◽  
Classification Algorithms ◽  
Machine Learning Classification ◽  
Information Models ◽  
Mining Methods ◽  
Do So

:Most online customers use cards to pay for their purchases. As charge cards become the most mainstream strategy for installment, instances of misrepresentation relationship with it too increases. The primary goal of this venture is to be ready to perceive false exchanges from non-fake exchanges. In request to do so,primarily,data mining methods are utilized to examine the examples and attributes of deceitful and non-fake transactions.Then,machine learning systems are utilized to foresee the fake and non-fake exchanges automatically. Algorithms LR (Logistic Regression) is used. Therefore, the blend of AI and information mining procedures are utilized to distinguish the fake and non-fake exchanges by learning the examples of the information. Models are made utilizing these calculations and afterward precision,accuracy,recall are determined and an examination is made.

scholarly journals Ensemble Data Mining Methods

2008 ◽  
pp. 356-363 ◽  
Author(s):  
Nikunj C. Oza
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Prediction Accuracy ◽  
Ensemble Methods ◽  
Multiple Models ◽  
Machine Learning Methods ◽  
Ensemble Data ◽  
Basic Goal ◽  
Mining Methods ◽  
The Individual

Ensemble data mining methods, also known as committee methods or model combiners, are machine learning methods that leverage the power of multiple models to achieve better prediction accuracy than any of the individual models could on their own. The basic goal when designing an ensemble is the same as when establishing a committee of people: Each member of the committee should be as competent as possible, but the members should complement one another. If the members are not complementary, that is, if they always agree, then the committee is unnecessary — any one member is sufficient. If the members are complementary, then when one or a few members make an error, the probability is high that the remaining members can correct this error. Research in ensemble methods has largely revolved around designing ensembles consisting of competent yet complementary models.

scholarly journals Data Mining applied on Web Robots Detection: A Systematic Mapping

2021 ◽  
Author(s):  
Ramon Abilio ◽  
Cristiano Garcia ◽  
Victor Fernandes
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Learning Algorithms ◽  
Web Server ◽  
Machine Learning Algorithms ◽  
Web Pages ◽  
Systematic Mapping ◽  
Mining Methods ◽  
Server Logs ◽  
Web Server Logs

Browsing on Internet is part of the world population’s daily routine. The number of web pages is increasing and so is the amount of published content (news, tutorials, images, videos) provided by them. Search engines use web robots to index web contents and to offer better results to their users. However, web robots have also been used for exploiting vulnerabilities in web pages. Thus, monitoring and detecting web robots’ accesses is important in order to keep the web server as safe as possible. Data Mining methods have been applied to web server logs (used as data source) in order to detect web robots. Then, the main objective of this work was to observe evidences of definition or use of web robots detection by analyzing web server-side logs using Data Mining methods. Thus, we conducted a systematic Literature mapping, analyzing papers published between 2013 and 2020. In the systematic mapping, we analyzed 34 studies and they allowed us to better understand the area of web robots detection, mapping what is being done, the data used to perform web robots detection, the tools, and algorithms used in the Literature. From those studies, we extracted 33 machine learning algorithms, 64 features, and 13 tools. This study is helpful for researchers to find machine learning algorithms, features, and tools to detect web robots by analyzing web server logs.

scholarly journals Data mining methods of healthy indoor climate coefficients for comfortable well-being

2018 ◽  
Vol 29 (3) ◽  
pp. 7-12
Author(s):  
Grit Behrens ◽  
Klaus Schlender ◽  
Florian Fehring
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Indoor Air ◽  
Well Being ◽  
Machine Learning Algorithms ◽  
Indoor Climate ◽  
Measurement And Analysis ◽  
Scientific Project ◽  
Mining Methods ◽  
Analysis System

Abstract This article provides information about a currently developed measurement and analysis system ‘Smart Monitoring’, which is used on scientific project in terms of healthy indoor air coefficients, as well as the processing of the collected data for machine learning algorithms. The target is to reduce CO2 emissions caused by wrong ventilation habits in building sector after renovation process in older buildings.

Big Data and Service Science

2014 ◽  
pp. 128-144 ◽  
Author(s):  
Tu-Bao Ho ◽  
Siriwon Taewijit ◽  
Quang-Bach Ho ◽  
Hieu-Chi Dam
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Customer Relationship Management ◽  
Relationship Management ◽  
Service Science ◽  
Customer Relationship ◽  
Service Value ◽  
Mining Methods ◽  
The Relationship

Big data is about handling huge and/or complex datasets that conventional technologies cannot handle or handle well. Big data is currently receiving tremendous attention from both industry and academia as there is much more data around us than ever before. This chapter addresses the relationship between big data and service science, especially how big data can contribute to the process of co-creation of service value. In particular, the value co-creation in terms of customer relationship management is mentioned. The chapter starts with brief descriptions of big data, machine learning and data mining methods, service science and its model of value co-creation, and then addresses the key idea of how big data can contribute to co-create service value.

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