document

Minimize experimentation overhead through dataset selection, ensemble feature attention, and feature selection with reduced subset sizes

Anton, Mihai (author)

In large-scale ML, data size becomes a critical variable, especially in the context of large companies, where models already exist and are hard to change and fine-tune. Time to market and model quality are essential metrics, thus looking for ways to select, prune and augment the input data while treating the model as a black box can speed up the...

master thesis 2024

document

Daily Life Activity Recognition with a Head Mounted IMU on Older Adults: Which Features to Extract?

Raizman, Omri (author)

As the population aged 65 and above increases, falls among these older adults emerge as a significant public health concern, leading to disabil- ities and economic burdens. Preventative strategies and personalized fall risk assessments are essential for mitigating fall risks. Human Activity Recognition in early fall risk detection by monitoring...

master thesis 2023

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Automatic feature discovery: A comparative study between filter and wrapper feature selection techniques

Mânăstireanu, Andrei (author)

The curse of dimensionality is a common challenge in machine learning, and feature selection techniques are commonly employed to address this issue by selecting a subset of relevant features. However, there is no consistently superior approach for choosing the most significant subset of features. We conducted a comprehensive analysis comparing...

bachelor thesis 2023

document

Filtering Knowledge: A Comparative Analysis of Information-Theoretical-Based Feature Selection Methods

Vasilev, Kiril (author)

The data used in machine learning algorithms strongly influences the algorithms' capabilities. Feature selection techniques can choose a set of columns that meet a certain learning goal. There is a wide variety of feature selection methods, however, the ones we cover in this comparative analysis are part of the information-theoretical-based...

bachelor thesis 2023

document

Data-Driven Empirical Analysis of Correlation-Based Feature Selection Techniques

Buşe, Florena (author)

Thus far the democratization of machine learning, which resulted in the field of AutoML, has focused on the automation of model selection and hyperparameter optimization. Nevertheless, the need for high-quality databases to increase performance has sparked interest in correlation-based feature selection, a simple and fast, yet effective approach...

bachelor thesis 2023

document

A comparative study for using PCA, LDA, GDA, and Lasso for dimensionality reduction before classification algorithms

Anceaux, Duyemo (author)

Since every day more and more data is collected, it becomes more and more expensive to process. To reduce these costs, you can use dimensionality reduction to reduce the number of features per instance in a given dataset. <br/><br/>In this paper, we will compare four possible methods of dimensionality reduction. The feature extraction methods...

bachelor thesis 2023

document

Unveiling microbial biomarkers of ruminant methane emission through machine learning

Peng, C. (author), May, Ali (author), Abeel, T.E.P.M.F. (author)

BackgroundEnteric methane from cow burps, which results from microbial fermentation of high-fiber feed in the rumen, is a significant contributor to greenhouse gas emissions. A promising strategy to address this problem is microbiome-based precision feed, which involves identifying key microorganisms for methane production. While machine...

journal article 2023

document

Data-driven augmentation of a RANS turbulence model for transonic flow prediction

Grabe, Cornelia (author), Jäckel, Florian (author), Khurana, Parv (author), Dwight, R.P. (author)

Purpose: This paper aims to improve Reynolds-averaged Navier Stokes (RANS) turbulence models using a data-driven approach based on machine learning (ML). A special focus is put on determining the optimal input features used for the ML model. Design/methodology/approach: The field inversion and machine learning (FIML) approach is applied to...

journal article 2023

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“How sweet are your strawberries?”: Predicting sugariness using non-destructive and affordable hardware

Wen, J. (author), Abeel, T.E.P.M.F. (author), de Weerdt, M.M. (author)

Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to obtain quality estimations in a consistent and...

journal article 2023

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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

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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

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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

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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

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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

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

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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, Jan (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