Adaptive Methods in Machine Learning

2016 ◽  
pp. 207-232 ◽  
Author(s):  
Maureen Clerc ◽  
Emmanuel Daucé ◽  
Jérémie Mattout
Keyword(s):  
Machine Learning ◽  
Adaptive Methods

Principled Machine Learning Using the Super Learner: An Application to Predicting Prison Violence

2018 ◽  
Vol 48 (3) ◽  
pp. 698-721 ◽  
Author(s):  
Valerio Baćak ◽  
Edward H. Kennedy
Keyword(s):  
Machine Learning ◽  
Social Science ◽  
Research Question ◽  
Social Science Research ◽  
Science Research ◽  
Adaptive Methods ◽  
Prison Violence ◽  
Data Set ◽  
Super Learner ◽  
Data Adaptive

A rapidly growing number of algorithms are available to researchers who apply statistical or machine learning methods to answer social science research questions. The unique advantages and limitations of each algorithm are relatively well known, but it is not possible to know in advance which algorithm is best suited for the particular research question and the data set at hand. Typically, researchers end up choosing, in a largely arbitrary fashion, one or a handful of algorithms. In this article, we present the Super Learner—a powerful new approach to statistical learning that leverages a variety of data-adaptive methods, such as random forests and spline regression, and systematically chooses the one, or a weighted combination of many, that produces the best forecasts. We illustrate the use of the Super Learner by predicting violence among inmates from the 2005 Census of State and Federal Adult Correctional Facilities. Over the past 40 years, mass incarceration has drastically weakened prisons’ capacities to ensure inmate safety, yet we know little about the characteristics of prisons related to inmate victimization. We discuss the value of the Super Learner in social science research and the implications of our findings for understanding prison violence.

scholarly journals Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning

2020 ◽  
Vol 108 (1) ◽  
pp. 86-109 ◽  
Author(s):  
Saiprasad Ravishankar ◽  
Jong Chul Ye ◽  
Jeffrey A. Fessler
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Adaptive Methods ◽  
Data Adaptive

Machine Learning Techniques for Enhancing Student Learning Experiences

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
R R Tribhuvan ◽  
T. Bhaskar
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Academic Staff ◽  
Learning Experiences ◽  
Adaptive Methods ◽  
Faculty Members ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Program Outcomes ◽  
Course Outcomes

Outcome-based learning (OBL) is a tried-and-true learning technique based on a set of predetermined objectives. Program Educational Objectives (PEOs), Program Outcomes (POs), and Course Outcomes are the three components of OBL (COs). Faculty members may adopt many ML-based advised actions at the conclusion of each course to improve the quality of learning and, as a result, the overall education. Due to the huge number of courses and faculty members involved, harmful behaviors may be advocated, resulting in unwanted and incorrect choices. The education system is described in this study based on college course requirements, academic records, and course learning results evaluations is provided for anticipating appropriate actions utilizing various machine learning algorithms. Dataset translates to different problem conversion methods and adaptive methods such as one-versus-all, binary significance, naming power set, series classification and custom classification ML-KNN. The suggested recommender ML-based system is used as a case study at the Institute of Computer and Information Sciences to assist academic staff in boosting learning quality and instructional methodologies. The results suggest that the proposed recommendation system offers more measures to improve students' learning experiences.

scholarly journals Learning from Imbalanced Data in Classification

2020 ◽  
Vol 8 (5) ◽  
pp. 1907-1916
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Hybrid Approach ◽  
Real Life ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Adaptive Methods ◽  
Imbalanced Datasets ◽  
Ensemble Technique ◽  
Data Level

Imbalanced data learning is a research area and day by day development is going on. Due to these researchers are motivated to pay attention to find efficient and adaptive methods for real-world problems. Machine learning, as well as data mining, is a field where researchers are finding different methods to solve problems related to imbalanced datasets and also the challenges faced in day to day life. The uneven class distribution in the dataset is the reason behind the degradation of performance in approaches used by data mining as well as machine learning. Continuous advancements of machine learning as well as mining data combining it with big data, a deep insight is required to understand the nature of learning imbalanced data. New challenges are emerging due to this development. Among the two approaches algorithm level and data level, the most popular approach compared to this is the hybrid approach. It is found that there is a bias for the majority class which affects the decision making task and overall accuracy of classification. The ensemble method is an efficient technique to deal with the uneven distribution of data. The aim of the paper is to presents the overview of class imbalance problems, solutions to handle it, open issues and challenges in learning imbalanced datasets. Based on the experiment conducted on one dataset it is found that ensemble technique along with other data-level methods gives good results. This hybrid method can be applied in many real-life applications like software defect prediction, behavior analysis, intrusion detection, medical diagnosis, etc. The paper further provides research directions in learning from the imbalanced dataset.

scholarly journals Benchmarking machine learning algorithms for adaptive quantum phase estimation with noisy intermediate-scale quantum sensors

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nelson Filipe Costa ◽  
Omar Yasser ◽  
Aidar Sultanov ◽  
Gheorghe Sorin Paraoanu
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Adaptive Methods ◽  
Machine Learning Algorithms ◽  
Trapped Ions ◽  
Quantum Phase ◽  
Nitrogen Vacancy ◽  
Phase Estimation ◽  
Fringe Visibility ◽  
Swarm Optimization

AbstractQuantum phase estimation is a paradigmatic problem in quantum sensing and metrology. Here we show that adaptive methods based on classical machine learning algorithms can be used to enhance the precision of quantum phase estimation when noisy non-entangled qubits are used as sensors. We employ the Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms to this task and we identify the optimal feedback policies which minimize the Holevo variance. We benchmark these schemes with respect to scenarios that include Gaussian and Random Telegraph fluctuations as well as reduced Ramsey-fringe visibility due to decoherence. We discuss their robustness against noise in connection with real experimental setups such as Mach–Zehnder interferometry with optical photons and Ramsey interferometry in trapped ions, superconducting qubits and nitrogen-vacancy (NV) centers in diamond.

scholarly journals 314Data-adaptive methods for high-dimensional mediation analysis: Application to a randomised trial of tuberculosis vaccination

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Margarita Moreno-Betancur ◽  
Nicole L Messina ◽  
Kaya Gardiner ◽  
Nigel Curtis ◽  
Stijn Vansteelandt
Keyword(s):  
Machine Learning ◽  
Statistical Inference ◽  
Mediation Analysis ◽  
Learning Algorithms ◽  
Adaptive Methods ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Data Adaptive ◽  
Randomised Controlled ◽  
Doubly Robust

Abstract Focus of Presentation Statistical methods for causal mediation analysis are useful for understanding the pathways by which a certain treatment or exposure impacts health outcomes. Existing methods necessitate modelling of the distribution of the mediators, which quickly becomes infeasible when mediators are high-dimensional (e.g., biomarkers). We propose novel data-adaptive methods for estimating the indirect effect of a randomised treatment that acts via a pathway represented by a high-dimensional set of measurements. This work was motivated by the Melbourne Infant Study: BCG for Allergy and Infection Reduction (MIS BAIR), a randomised controlled trial investigating the effect of neonatal tuberculosis vaccination on clinical allergy and infection outcomes, and its mechanisms of action. Findings The proposed methods are doubly robust, which allows us to achieve (uniformly) valid statistical inference, even when machine learning algorithms are used for the two required models. We illustrate these in the context of the MIS BAIR study, investigating the mediating role of immune pathways represented by a high-dimensional vector of cytokine responses under various stimulants. We confirm adequate performance of the proposed methods in an extensive simulation study. Conclusions/Implications The proposed methods provide a feasible and flexible analytic strategy for examining high-dimensional mediators in randomised controlled trials. Key messages Data-adaptive methods for mediation analysis are desirable in the context of high-dimensional mediators, such as biomarkers. We propose novel doubly robust methods, which enable valid statistical inference when using machine learning algorithms for estimation.

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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