Sökning: "Imbalanced classification data"

Visar resultat 1 - 5 av 65 uppsatser innehållade orden Imbalanced classification data.

  1. 1. Detecting Fraudulent User Behaviour : A Study of User Behaviour and Machine Learning in Fraud Detection

    Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Analys och partiella differentialekvationer

    Författare :Patrik Gerdelius; Sjönneby Hugo; [2024]
    Nyckelord :Fraud Detection; User Behaviour; Random Forest; PCA; SMOTE;

    Sammanfattning : This study aims to create a Machine Learning model and investigate its performance of detecting fraudulent user behaviour on an e-commerce platform. The user data was analysed to identify and extract critical features distinguishing regular users from fraudulent users. LÄS MER

  3. 2. Improving echocardiogram view classification using diffusion models

    Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

    Författare :Luis Arevalo; Anouka Ranby; [2023-10-23]
    Nyckelord :Computer; science; computer science; engineering; project; artificial intelligence; machine learning; deep neural networks; diffusion models; synthetic data; echocardiogram classification;

    Sammanfattning : In the field of medical science datasets are often highly imbalanced, where rare datapoints are of high importance. This study aims to explore the usage of synthetic datasets to improve the classification of echocardiogram views. LÄS MER

  4. 3. Generating an Interpretable Ranking Model: Exploring the Power of Local Model-Agnostic Interpretability for Ranking Analysis

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

    Författare :Laura Galera Alfaro; [2023]
    Nyckelord :Explainable Artificial Intelligence; Learning To Rank; Local ModelAgnostic Interpretability; Ranking Generalized Additive Models;

    Sammanfattning : Machine learning has revolutionized recommendation systems by employing ranking models for personalized item suggestions. However, the complexity of learning-to-rank (LTR) models poses challenges in understanding the underlying reasons contributing to the ranking outcomes. LÄS MER

  5. 4. 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

  7. 5. Multi-Class Classification for Predicting Customer Satisfaction : Application of machine learning methods to predict customer satisfaction at IKEA

    Uppsats för yrkesexamina på avancerad nivå, Umeå universitet/Institutionen för matematik och matematisk statistik

    Författare :Stina Backerholm; Malin Börjesjö; [2023]
    Nyckelord :Multi-Class Classification; Imbalanced Data; Machine Learning; Multi-Klass Klassifisering; Obalanserat Data; Maskininlärning;

    Sammanfattning : Gaining a comprehensive understanding of the features that contribute to customer satisfaction after contact with IKEA’s Remote Customer Meeting Points (RCMPs) is essential for implementing effective remedial measures in the future. The aim of this project is to investigate if it is possible to find key features that influence customer satisfaction and to use these to predict customer satisfaction. LÄS MER