Thesis work submitted to Faculty of Applied Computer Sciences and Biosciences at the University of Applied Sciences.
- Prof. Dr. Thomas Villmann
- Msc. Jensun Ravichandran
Classification label security determines the extent to which predicted labels from classification results can be trusted. The uncertainty surrounding classification labels is resolved by the security to which the classification is made. Therefore, classification label security is very significant for decision-making whenever we are encountered with a classification task. This thesis investigates the determination of the classification label security by utilizing fuzzy probabilistic assignments of Fuzzy c-means. The investigation is accompanied by implementation, experimentation, visualization and documentation of the results.
The Thesis implementaion has been done in Python and can be found here: Classification-Label-Security-Certainty
- Introduction
- Motivation
- Brief on Clustering
- Objective Function Clustering
- Fuzzy c-Means
- Introduction to Learning Vector Quantization
- Generalized Learning Vector Quantization
- Generalized Matrix Learning Vector Quantization
- Cross-Entropy in Learning Vector Quantization
- Soft Learning Vector Quantization
- Robust Soft Learning Vector Quantization with Cross-Entropy Optimization
- Classification Label Security/Certainty
- General Overview of Train/Test Procedure
- Iris Data Set
- Classification Label Security of Iris Data set
- Breast Cancer Wisconsin (Diagnostic) Data set (WDBC)
- Classification Label Security of Breast Cancer Wisconsin(Diagnostic) Data set
- Conclusion
- Reference Implementation in Python