Performance Evaluation for Machine Learning and Deep Learning Algorithms on Imbalanced Dataset: Case Study of Business Support System
| Autor: | Yen-Cheng Lin, 林彥呈 |
|---|---|
| Rok vydání: | 2018 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 106 The data collected from the real systems is imbalanced, i.e. The classification categories are not equally represented. The existing classification algorithms usually introduce bias towards majority class (potentially uninteresting class). In this thesis, we will apply the anomaly prediction on a Business Support System (BSS) [1] of telecommunication service providers as a case to study the performance of the machine learning [2, 3, 4, 5] and deep learning [2, 3, 4] algorithms on imbalanced dataset. The reliability and stability have been treated as the major requirements for a BSS [6]. In other words, the occurrences of anomaly are rare events in a BSS. The distribution of the system log data of BSS is highly imbalanced. Thus, it is more challenging for machine learning algorithms and deep learning algorithms to have good performance on highly imbalanced datasets. To resolve the issue, we propose an approach, namely Frequency-based Feature Creation (FFC), to create new features to describe the distributions of the one-hot-encoded features. Furthermore, we enhance some existing techniques to amplify the effects of the minority class, e.g., Voting with Threshold (VT) and Classification Correction (CC). |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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