scholarly journals Linking Human And Machine Behavior: A New Approach to Evaluate Training Data Quality for Beneficial Machine Learning

2021 ◽  
Author(s):  
Thilo Hagendorff
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Ethical Perspective ◽  
New Approach ◽  
Specific Objective ◽  
Machine Learning Applications ◽  
Socially Relevant

AbstractMachine behavior that is based on learning algorithms can be significantly influenced by the exposure to data of different qualities. Up to now, those qualities are solely measured in technical terms, but not in ethical ones, despite the significant role of training and annotation data in supervised machine learning. This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications. Based on the rationale that different social and psychological backgrounds of individuals correlate in practice with different modes of human–computer-interaction, the paper describes from an ethical perspective how varying qualities of behavioral data that individuals leave behind while using digital technologies have socially relevant ramification for the development of machine learning applications. The specific objective of this study is to describe how training data can be selected according to ethical assessments of the behavior it originates from, establishing an innovative filter regime to transition from the big data rationale n = all to a more selective way of processing data for training sets in machine learning. The overarching aim of this research is to promote methods for achieving beneficial machine learning applications that could be widely useful for industry as well as academia.

scholarly journals Data Quality Measures and Efficient Evaluation Algorithms for Large-Scale High-Dimensional Data

2021 ◽  
Vol 11 (2) ◽  
pp. 472
Author(s):  
Hyeongmin Cho ◽  
Sangkyun Lee
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Large Scale ◽  
High Dimensional Data ◽  
Quality Measures ◽  
Training Data ◽  
Measure Data ◽  
High Dimensional ◽  
Small Scale ◽  
Class Separability

Machine learning has been proven to be effective in various application areas, such as object and speech recognition on mobile systems. Since a critical key to machine learning success is the availability of large training data, many datasets are being disclosed and published online. From a data consumer or manager point of view, measuring data quality is an important first step in the learning process. We need to determine which datasets to use, update, and maintain. However, not many practical ways to measure data quality are available today, especially when it comes to large-scale high-dimensional data, such as images and videos. This paper proposes two data quality measures that can compute class separability and in-class variability, the two important aspects of data quality, for a given dataset. Classical data quality measures tend to focus only on class separability; however, we suggest that in-class variability is another important data quality factor. We provide efficient algorithms to compute our quality measures based on random projections and bootstrapping with statistical benefits on large-scale high-dimensional data. In experiments, we show that our measures are compatible with classical measures on small-scale data and can be computed much more efficiently on large-scale high-dimensional datasets.

scholarly journals Effects of Training Set Size on Supervised Machine-Learning Land-Cover Classification of Large-Area High-Resolution Remotely Sensed Data

2021 ◽  
Vol 13 (3) ◽  
pp. 368
Author(s):  
Christopher A. Ramezan ◽  
Timothy A. Warner ◽  
Aaron E. Maxwell ◽  
Bradley S. Price
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Remotely Sensed ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Sample Sizes ◽  
Remotely Sensed Data ◽  
Large Area ◽  
Training Set ◽  
Set Size

The size of the training data set is a major determinant of classification accuracy. Nevertheless, the collection of a large training data set for supervised classifiers can be a challenge, especially for studies covering a large area, which may be typical of many real-world applied projects. This work investigates how variations in training set size, ranging from a large sample size (n = 10,000) to a very small sample size (n = 40), affect the performance of six supervised machine-learning algorithms applied to classify large-area high-spatial-resolution (HR) (1–5 m) remotely sensed data within the context of a geographic object-based image analysis (GEOBIA) approach. GEOBIA, in which adjacent similar pixels are grouped into image-objects that form the unit of the classification, offers the potential benefit of allowing multiple additional variables, such as measures of object geometry and texture, thus increasing the dimensionality of the classification input data. The six supervised machine-learning algorithms are support vector machines (SVM), random forests (RF), k-nearest neighbors (k-NN), single-layer perceptron neural networks (NEU), learning vector quantization (LVQ), and gradient-boosted trees (GBM). RF, the algorithm with the highest overall accuracy, was notable for its negligible decrease in overall accuracy, 1.0%, when training sample size decreased from 10,000 to 315 samples. GBM provided similar overall accuracy to RF; however, the algorithm was very expensive in terms of training time and computational resources, especially with large training sets. In contrast to RF and GBM, NEU, and SVM were particularly sensitive to decreasing sample size, with NEU classifications generally producing overall accuracies that were on average slightly higher than SVM classifications for larger sample sizes, but lower than SVM for the smallest sample sizes. NEU however required a longer processing time. The k-NN classifier saw less of a drop in overall accuracy than NEU and SVM as training set size decreased; however, the overall accuracies of k-NN were typically less than RF, NEU, and SVM classifiers. LVQ generally had the lowest overall accuracy of all six methods, but was relatively insensitive to sample size, down to the smallest sample sizes. Overall, due to its relatively high accuracy with small training sample sets, and minimal variations in overall accuracy between very large and small sample sets, as well as relatively short processing time, RF was a good classifier for large-area land-cover classifications of HR remotely sensed data, especially when training data are scarce. However, as performance of different supervised classifiers varies in response to training set size, investigating multiple classification algorithms is recommended to achieve optimal accuracy for a project.

scholarly journals The Role of Textualisation and Argumentation in Understanding the Machine Learning Process

2017 ◽  
Author(s):  
Kacper Sokol ◽  
Peter Flach
Keyword(s):  
Machine Learning ◽  
Predictive Accuracy ◽  
Spatial Perception ◽  
Black Box ◽  
High Dimensional ◽  
Box Models ◽  
Machine Learning Applications ◽  
Black Box Models ◽  
Machine Learning Models

Understanding data, models and predictions is important for machine learning applications. Due to the limitations of our spatial perception and intuition, analysing high-dimensional data is inherently difficult. Furthermore, black-box models achieving high predictive accuracy are widely used, yet the logic behind their predictions is often opaque. Use of textualisation -- a natural language narrative of selected phenomena -- can tackle these shortcomings. When extended with argumentation theory we could envisage machine learning models and predictions arguing persuasively for their choices.

Detecting Pressure Anomalies While Drilling Using a Machine Learning Hybrid Approach

2021 ◽  
Author(s):  
Aurore Lafond ◽  
Maurice Ringer ◽  
Florian Le Blay ◽  
Jiaxu Liu ◽  
Ekaterina Millan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Quality ◽  
Real Time ◽  
Large Scale ◽  
Hybrid Approach ◽  
Physical Models ◽  
Training Data ◽  
Digital Data ◽  
Machine Learning Techniques ◽  
New System

Abstract Abnormal surface pressure is typically the first indicator of a number of problematic events, including kicks, losses, washouts and stuck pipe. These events account for 60–70% of all drilling-related nonproductive time, so their early and accurate detection has the potential to save the industry billions of dollars. Detecting these events today requires an expert user watching multiple curves, which can be costly, and subject to human errors. The solution presented in this paper is aiming at augmenting traditional models with new machine learning techniques, which enable to detect these events automatically and help the monitoring of the drilling well. Today’s real-time monitoring systems employ complex physical models to estimate surface standpipe pressure while drilling. These require many inputs and are difficult to calibrate. Machine learning is an alternative method to predict pump pressure, but this alone needs significant labelled training data, which is often lacking in the drilling world. The new system combines these approaches: a machine learning framework is used to enable automated learning while the physical models work to compensate any gaps in the training data. The system uses only standard surface measurements, is fully automated, and is continuously retrained while drilling to ensure the most accurate pressure prediction. In addition, a stochastic (Bayesian) machine learning technique is used, which enables not only a prediction of the pressure, but also the uncertainty and confidence of this prediction. Last, the new system includes a data quality control workflow. It discards periods of low data quality for the pressure anomaly detection and enables to have a smarter real-time events analysis. The new system has been tested on historical wells using a new test and validation framework. The framework runs the system automatically on large volumes of both historical and simulated data, to enable cross-referencing the results with observations. In this paper, we show the results of the automated test framework as well as the capabilities of the new system in two specific case studies, one on land and another offshore. Moreover, large scale statistics enlighten the reliability and the efficiency of this new detection workflow. The new system builds on the trend in our industry to better capture and utilize digital data for optimizing drilling.

Machine Learning and Value-Based Software Engineering

2012 ◽  
pp. 287-301
Author(s):  
Du Zhang
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Full Range ◽  
Training Data ◽  
Learning Methods ◽  
Engineering Research ◽  
Machine Learning Methods ◽  
Machine Learning Applications ◽  
Value Propositions ◽  
Software Engineering Research

Software engineering research and practice thus far are primarily conducted in a value-neutral setting where each artifact in software development such as requirement, use case, test case, and defect, is treated as equally important during a software system development process. There are a number of shortcomings of such value-neutral software engineering. Value-based software engineering is to integrate value considerations into the full range of existing and emerging software engineering principles and practices. Machine learning has been playing an increasingly important role in helping develop and maintain large and complex software systems. However, machine learning applications to software engineering have been largely confined to the value-neutral software engineering setting. In this paper, the general message to be conveyed is to apply machine learning methods and algorithms to value-based software engineering. The training data or the background knowledge or domain theory or heuristics or bias used by machine learning methods in generating target models or functions should be aligned with stakeholders’ value propositions. An initial research agenda is proposed for machine learning in value-based software engineering.

Design-Oriented Multifidelity Fluid Simulation Using Machine Learned Fidelity Mapping

2019 ◽  
Author(s):  
Kazuko Fuchi ◽  
Eric M. Wolf ◽  
David S. Makhija ◽  
Nathan A. Wukie ◽  
Christopher R. Schrock ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Fluid Simulation ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Supervised Machine Learning ◽  
High Fidelity ◽  
Computational Domain ◽  
Symmetry Properties ◽  
High Fidelity Simulations

Abstract A machine learning algorithm that performs multifidelity domain decomposition is introduced. While the design of complex systems can be facilitated by numerical simulations, the determination of appropriate physics couplings and levels of model fidelity can be challenging. The proposed method automatically divides the computational domain into subregions and assigns required fidelity level, using a small number of high fidelity simulations to generate training data and low fidelity solutions as input data. Unsupervised and supervised machine learning algorithms are used to correlate features from low fidelity solutions to fidelity assignment. The effectiveness of the method is demonstrated in a problem of viscous fluid flow around a cylinder at Re ≈ 20. Ling et al. built physics-informed invariance and symmetry properties into machine learning models and demonstrated improved model generalizability. Along these lines, we avoid using problem dependent features such as coordinates of sample points, object geometry or flow conditions as explicit inputs to the machine learning model. Use of pointwise flow features generates large data sets from only one or two high fidelity simulations, and the fidelity predictor model achieved 99.5% accuracy at training points. The trained model was shown to be capable of predicting a fidelity map for a problem with an altered cylinder radius. A significant improvement in the prediction performance was seen when inputs are expanded to include multiscale features that incorporate neighborhood information.

Sign in / Sign up

Export Citation Format

Share Document