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Enhancing Telecom Churn Prediction: Adaboost With Oversampling And Recursive Feature Elimination Approach, Long Dinh Tran
Enhancing Telecom Churn Prediction: Adaboost With Oversampling And Recursive Feature Elimination Approach, Long Dinh Tran
Master's Theses
Churn prediction is a critical task for businesses to retain their valuable customers. This paper presents a comprehensive study of churn prediction in the telecom sector using 15 approaches, including popular algorithms such as Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, and AdaBoost.
The study is segmented into three sets of experiments, each focusing on a different approach to building the churn prediction model. The model is constructed using the original training set in the first set of experiments. The second set involves oversampling the training set to address the issue of imbalanced data. Lastly, the third set …
Atrengine: An Orientation-Based Algorithm For Automatic Target Recognition, Justin Ting-Jeuan Kuo
Atrengine: An Orientation-Based Algorithm For Automatic Target Recognition, Justin Ting-Jeuan Kuo
Master's Theses
Automatic Target Recognition (ATR) is a subject involving the use of sensor data to develop an algorithm for identifying targets of significance. It is of particular interest in military applications such as unmanned aerial vehicles and missile tracking systems. This thesis develops an orientation-based classification approach from previous ATR algorithms for 2-D Synthetic Aperture Radar (SAR) images. Prior work in ATR includes Chessa Guilas’ Hausdorff Probabilistic Feature Analysis Approach in 2005 and Daniel Cary’s Optimal Rectangular Fit in 2007.
A system incorporating multiple modules performing different tasks is developed to streamline the data processing of previous algorithms. Using images from …