Sökning: "Obalanserade Dataset"
Visar resultat 1 - 5 av 14 uppsatser innehållade orden Obalanserade Dataset.
1. Credit Card Transaction Fraud Detection Using Neural Network Classifiers
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : With increasing usage of credit card payments, credit card fraud has also been increasing. Therefore a fast and accurate fraud detection system is vital for the banks. To solve the problem of fraud detection, different machine learning classifiers have been designed and trained on a credit card transaction dataset. LÄS MER
2. Predicting Customer Satisfaction in the Context of Last-Mile Delivery using Supervised and Automatic Machine Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : The prevalence of online shopping has steadily risen in the last few years. In response to these changes, last-mile delivery services have emerged that enable goods to reach customers within a shorter timeframe compared to traditional logistics providers. LÄS MER
3. Interaction-Aware Vehicle Trajectory Prediction via Attention Mechanism and Beyond
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : With the development of autonomous driving technology, vehicle trajectory prediction has become a hot topic in the intelligent traffic area. However, complex road conditions may bring multiple challenges to the vehicle trajectory prediction model. LÄS MER
4. Text Classification of Human Resources-related Data with Machine Learning
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Text classification has been an important application and research subject since the origin of digital documents. Today, as more and more data are stored in the form of electronic documents, the text classification approach is even more vital. LÄS MER
5. Multivariate Time Series Data Generation using Generative Adversarial Networks : Generating Realistic Sensor Time Series Data of Vehicles with an Abnormal Behaviour using TimeGAN
Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)Sammanfattning : Large datasets are a crucial requirement to achieve high performance, accuracy, and generalisation for any machine learning task, such as prediction or anomaly detection, However, it is not uncommon for datasets to be small or imbalanced since gathering data can be difficult, time-consuming, and expensive. In the task of collecting vehicle sensor time series data, in particular when the vehicle has an abnormal behaviour, these struggles are present and may hinder the automotive industry in its development. LÄS MER