Modern Statistical Modeling in Machine Learning and Big Data Analytics

Handbook of Research on Big Data Clustering and Machine Learning - Advances in Data Mining and Database Management ◽

10.4018/978-1-7998-0106-1.ch007 ◽

2020 ◽

pp. 135-151

Author(s):

Niloofar Ramezani

Keyword(s):

Machine Learning ◽

Big Data ◽

Statistical Modeling ◽

Statistical Models ◽

Data Analytics ◽

High Dimensional Data ◽

Big Data Analytics ◽

High Dimensional ◽

Statistical Techniques ◽

The Future

Machine learning, big data, and high dimensional data are the topics we hear about frequently these days, and some even call them the wave of the future. Therefore, it is important to use appropriate statistical models, which have been established for many years, and their efficiency has already been evaluated to contribute into advancing machine learning, which is a relatively newer field of study. Different algorithms that can be used within machine learning, depending on the nature of the variables, are discussed, and appropriate statistical techniques for modeling them are presented in this chapter.

Download Full-text

Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level

10.2172/1431303 ◽

2018 ◽

Cited By ~ 1

Author(s):

A. Ansari ◽

S. Mohaghegh ◽

M. Shahnam ◽

J. F. Dietiker ◽

T. Li

Keyword(s):

Machine Learning ◽

Fluid Dynamics ◽

Computational Fluid Dynamics ◽

Big Data ◽

Data Analytics ◽

Model Building ◽

Big Data Analytics ◽

Data Driven ◽

Cell Level

Download Full-text

Big Data Analytics of Identifying Geochemical Anomalies Supported by Machine Learning Methods

Natural Resources Research ◽

10.1007/s11053-017-9357-0 ◽

2017 ◽

Vol 27 (1) ◽

pp. 5-13 ◽

Cited By ~ 37

Author(s):

Renguang Zuo ◽

Yihui Xiong

Keyword(s):

Machine Learning ◽

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Learning Methods ◽

Machine Learning Methods ◽

Geochemical Anomalies

Download Full-text

Biases in machine learning models and big data analytics: The international criminal and humanitarian law implications

International Review of the Red Cross ◽

10.1017/s1816383121000096 ◽

2020 ◽

Vol 102 (913) ◽

pp. 199-234

Author(s):

Nema Milaninia

Keyword(s):

Machine Learning ◽

Human Rights ◽

Big Data ◽

Data Analytics ◽

International Criminal ◽

Big Data Analytics ◽

International Criminal Law ◽

Gender Inequalities ◽

Humanitarian Law ◽

Mass Graves

AbstractAdvances in mobile phone technology and social media have created a world where the volume of information generated and shared is outpacing the ability of humans to review and use that data. Machine learning (ML) models and “big data” analytical tools have the power to ease that burden by making sense of this information and providing insights that might not otherwise exist. In the context of international criminal and human rights law, ML is being used for a variety of purposes, including to uncover mass graves in Mexico, find evidence of homes and schools destroyed in Darfur, detect fake videos and doctored evidence, predict the outcomes of judicial hearings at the European Court of Human Rights, and gather evidence of war crimes in Syria. ML models are also increasingly being incorporated by States into weapon systems in order to better enable targeting systems to distinguish between civilians, allied soldiers and enemy combatants or even inform decision-making for military attacks.The same technology, however, also comes with significant risks. ML models and big data analytics are highly susceptible to common human biases. As a result of these biases, ML models have the potential to reinforce and even accelerate existing racial, political or gender inequalities, and can also paint a misleading and distorted picture of the facts on the ground. This article discusses how common human biases can impact ML models and big data analytics, and examines what legal implications these biases can have under international criminal law and international humanitarian law.

Download Full-text