document

Restoration of Missing Data using a Human Adaptive Framework: The Cleansing Algorithm

Dijkstra, Sjoerd (author)

Improving data quality is of the utmost importance for any data-driven company, as data quality is unmistakably tied to business analytics and processes. One method to improve upon data quality is to restore missing and wrong data entries.  </p><p class="MsoNormal">The goal of this research is construct an algorithm such...

master thesis 2022

document

From Feature Selection to Data Augmentation: the ADA Algorithm

Cruset Pla, Eduard (author)

The democratization of data science, and in particular of the machine learning pipeline, has focused on the automation of model selection, feature processing, and hyperparameter tuning. Nevertheless, the need for high-quality data for increased performance has sparked interest in the inclusion of data augmentation in these automatic machine...

bachelor thesis 2022

document

Automatic feature augmentation ranking: XGBoost

Neut, Oliver (author)

Automatic machine learning is a subfield of machine learning that automates the common procedures faced in predictive tasks. The problem of one such procedure is automatic data augmentation, where one desires to enrich the existing data to increase model performance. In relational data repositories, the data is stored in normal form. This causes...

bachelor thesis 2022

document

A Classification-Based Machine Learning Approach to the Prediction of Cyanobacterial Blooms in Chilgok Weir, South Korea

Kim, J. (author), Jonoski, Andreja (author), Solomatine, D.P. (author)

Cyanobacterial blooms appear by complex causes such as water quality, climate, and hydrological factors. This study aims to present the machine learning models to predict occurrences of these complicated cyanobacterial blooms efficiently and effectively. The dataset was classified into groups consisting of two, three, or four classes based on...

journal article 2022

document

Energy Efficient EPB Design Applying Machine Learning Techniques

Glab, K.B. (author), Wehrmeyer, G. (author), Thewes, M (author), Broere, W. (author)

A significant part of the energy consumed during the tunnelling process of Earth Pressure Balanced (EPB) Tunnel Boring Machines (TBMs) is related to the main drive, consisting of a set of motors driving the rotation of the cutting wheel. An energy efficient EPB design requires the optimization of the main drive to avoid over- or under powering...

conference paper 2022

document

Exploring Feature Selection Scenarios for Deep Learning-based Side-channel Analysis

Perin, G. (author), Wu, L. (author), Picek, S. (author)

One of the main promoted advantages of deep learning in profiling side-channel analysis is the possibility of skipping the feature engineering process. Despite that, most recent publications consider feature selection as the attacked interval from the side-channel measurements is pre-selected. This is similar to the worst-case security...

journal article 2022

document

Data-driven Steering Torque Behaviour Modelling with Hidden Markov Models

van Wijk, Robert (author), Lazcano, Andrea Michelle Rios (author), Akutain, Xabier Carrera (author), Shyrokau, B. (author)

Modern Advanced Driver Assistance Systems (ADAS) are limited in their ability to consider the driver's intention, resulting in unnatural guidance and low customer acceptance. In this research, we focus on a novel data-driven approach to predict driver steering torque. In particular, driver behavior is modeled by learning the parameters of a...

journal article 2022

document

A Two-Step Feature Extraction Algorithm: Application to deep learning for point cloud classification

Nurunnabi, A. (author), Teferle, F. N. (author), Laefer, D. F. (author), Lindenbergh, R.C. (author), Hunegnaw, A. (author)

Most deep learning (DL) methods that are not end-to-end use several multi-scale and multi-type hand-crafted features that make the network challenging, more computationally intensive and vulnerable to overfitting. Furthermore, reliance on empirically-based feature dimensionality reduction may lead to misclassification. In contrast, efficient...

journal article 2022

document

Data-driven Steering-Torque Behaviour Modelling: A Hidden Markov Model Approach

van Wijk, Robert (author)

Current commercial Driver Steering Assistance Systems (DSAS) focus on path-tracking performance without taking into account driver intentions. Improved driver-automation interaction can be achieved by sharing vehicle lateral control through torques. Furthermore, integrating a driver steering-torque model allows to better match driver intentions....

master thesis 2021

document

Utility metric for unsupervised feature selection

Villa, Amalia (author), Narayanan, Abhijith Mundanad (author), Van Huffel, Sabine (author), Bertrand, Alexander (author), Varon, Carolina (author)

Feature selection techniques are very useful approaches for dimensionality reduction in data analysis. They provide interpretable results by reducing the dimensions of the data to a subset of the original set of features. When the data lack annotations, unsupervised feature selectors are required for their analysis. Several algorithms for...

journal article 2021

document

A multi-measure feature selection algorithm for efficacious intrusion detection

Herrera-Semenets, Vitali (author), Bustio-Martínez, Lázaro (author), Hernández-León, Raudel (author), van den Berg, J. (author)

Every day the number of devices interacting through telecommunications networks grows resulting into an increase in the volume of data and information generated. At the same time, a growing number of information security incidents is being observed including the occurrence of unauthorized accesses, also named intrusions. As a consequence of...

journal article 2021

document

A causality based feature selection approach for data-driven dynamic security assessment

Bellizio, Federica (author), Cremer, Jochen (author), Sun, Mingyang (author), Strbac, Goran (author)

The integration of renewable energy sources increases the operational uncertainty of electric power systems and can lead to more frequent dynamic phenomena. The use of classifiers from machine learning is promising to include dynamics in the security assessment of the power system. The training of these classifiers is typically performed offline...

journal article 2021

document

Assessing the signal quality of electrocardiograms from varied acquisition sources: A generic machine learning pipeline for model generation

Albaba, Adnan (author), Simões-Capela, Neide (author), Wang, Yuyang (author), Hendriks, R.C. (author), De Raedt, Walter (author), Van Hoof, Chris (author)

Background and objective: Long-term electrocardiogram monitoring comes at the expense of signal quality. During unconstrained movements, the electrocardiogram is often corrupted by motion artefacts, which can lead to inaccurate physiological information. In this situation, automated quality assessment methods are useful to increase the...

journal article 2021

document

Partial Discharges and Noise Discrimination Using Magnetic Antennas, the Cross Wavelet Transform and Support Vector Machines

Muñoz Muñoz, F.A. (author), Mor, A. R. (author)

This paper presents a wavelet analysis technique together with support vector machines (SVM) to discriminate partial discharges (PD) from external disturbances (electromagnetic noise) in a GIS PD measuring system based on magnetic antennas. The technique uses the Cross Wavelet Transform (XWT) to process the PD signals and the external...

journal article 2020

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GPU Implementation of Grid Search based Feature Selection: Using Machine Learning to Predict Hydrocarbons using High Dimensional Datasets

Ament, Tjalling (author)

To optimize the exploitation of oil and gas reservoirs both on- and offshore, Biodentfiy has developed a method to predict prospectivity of hydrocarbons before drilling. This method uses microbiological DNA analysis of shallow soil or seabed samples to detect vertical upward microseepage from hydrocarbon accumulations, which change the...

master thesis 2020

document

A Machine Learning Approach to Evaluating Aircraft Deviations from Planned Routes

Sakyi-Gyinae, Master (author)

With the increasing trend in air traffic demand and evidence of large deviations from filed flight plans, airspace capacity is not being optimally utilized. In order to improve air traffic flow and capacity management systems, so that air traffic control operators can handle more aircraft safely, air traffic predictability needs to be improved....

master thesis 2019

document

Short-term forecasting of non-stationary time series using multiple feature selection methods

Özyar, Muhammed Imran (author)

Time series forecasting has been proved to be relatively easier for stationary time series, compared to non-stationary time series. This research proposes a method to partially omit the non-stationarity of the data using prioritized sampling. Using multiple feature selection methods in combination with a random forest regressor (RFR), we aim to...

bachelor thesis 2019

document

Influence of inland vessel stern shape aspects on propulsive performance: Derivation of insights and guidelines based on a computational study

Rotteveel, E. (author)

This research focuses on identifying the most important stern shape aspects, with regard to resistance and propulsion power, of inland ships. Such information should help designers to determine which hull form aspect to adjust in case design requirements need them to do so. The information is obtained by firstly conducting a large series of CFD...

doctoral thesis 2019

document

A Systematic Evaluation of Profiling Through Focused Feature Selection

Picek, S. (author), Heuser, Annelie (author), Jovic, Alan (author), Batina, Lejla (author)

Profiled side-channel attacks consist of several steps one needs to take. An important, but sometimes ignored, step is a selection of the points of interest (features) within side-channel measurement traces. A large majority of the related works start the analyses with an assumption that the features are preselected. Contrary to this...

journal article 2019

document

An automated ECG signal quality assessment method with supervised learning algorithm

Wang, Yuyang (author)

Wearable health has become a striking area in our daily life.<br/>Electrocardiogram (ECG) is one of the biomedical signals collected by the wearable or portable devices, which is widely used in heart rate monitoring and cardiac diagnosis. However, automatic ECG signal analysis is difficult in real application because the signals are easy to be...

master thesis 2018