Sökning: "Class Imbalance"

Visar resultat 1 - 5 av 62 uppsatser innehållade orden Class Imbalance.

  1. 1. Optimizing ERP Recommendations Using Machine Learning Techniques

    Kandidat-uppsats, Linnéuniversitetet/Institutionen för datavetenskap och medieteknik (DM)

    Författare :Ante Jeremiah; [2023]
    Nyckelord :Machine Learning; Imbalanced;

    Sammanfattning : This study explores the application of a recommendation engine in collaboration with Fortnox. The primary focus of this paper is to find potential improvements for their recommendation engine in terms of accurate recommendation for users. LÄS MER

  3. 2. Using Machine Learning to Optimize Near-Earth Object Sighting Data at the Golden Ears Observatory

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Laura Murphy; [2023]
    Nyckelord :Near-Earth Object Detection; Machine Learning; Deep Learning; Visual Transformers;

    Sammanfattning : This research project focuses on improving Near-Earth Object (NEO) detection using advanced machine learning techniques, particularly Vision Transformers (ViTs). The study addresses challenges such as noise, limited data, and class imbalance. LÄS MER

  4. 3. Neural Networks for Predictive Maintenance on Highly Imbalanced Industrial Data

    Master-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

    Författare :Oscar Montilla Tabares; [2023]
    Nyckelord :Class Imbalance; Cost Sensitivity; Cost-Sensitive Learning; Focal Loss; Binary Classification; Machine Learning; Deep Learning;

    Sammanfattning : Preventive maintenance plays a vital role in optimizing industrial operations. However, detecting equipment needing such maintenance using available data can be particularly challenging due to the class imbalance prevalent in real-world applications. LÄS MER

  5. 4. Improving Visibility Forecasts in Denmark Using Machine Learning Post-processing

    Master-uppsats, Uppsala universitet/Luft-, vatten- och landskapslära

    Författare :August Thomasson; [2023]
    Nyckelord :visibility forecast; fog; machine learning; numerical weather predicition; XGBoost; Random Forest; siktprognos; dimma; maskininlärning; numerisk vädermodell; XGBoost; Random Forest;

    Sammanfattning : Accurate fog prediction is an important task facing forecast centers since low visibility can affect anthropogenic systems, such as aviation. Therefore, this study investigates the use of Machine Learning classification algorithms for post-processing the output of the Danish Meteorological Institute’s operational Numerical Weather Prediction (NWP) model to improve visibility prediction. LÄS MER

  7. 5. Enhancing Neural Network Accuracy on Long-Tailed Datasets through Curriculum Learning and Data Sorting

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Daniel Barreira; [2023]
    Nyckelord :Machine Learning; Neural Network; CORAL-framework; Long-Tailed Data; Imbalance Metrics; Teacher-Student models; Curriculum Learning; Training Scheme; Maskininlärning; Neuralt Nätverk; CORAL-ramverk; Long-Tailed Data; Imbalance Metrics; Teacher-Student modeler; Curriculum Learning; Tränings- scheman;

    Sammanfattning : In this paper, a study is conducted to investigate the use of Curriculum Learning as an approach to address accuracy issues in a neural network caused by training on a Long-Tailed dataset. The thesis problem is presented by a Swedish e-commerce company. LÄS MER