Prototype LearningDuring my masters, I worked on prototype learning. Created the CLIFF algorithm and applied it to the forensic science domain as a forensic interpretation model.
Prototype learning algorithms are designed to eliminated the drawbacks of the K-nearest neighbor algorithm:
- The high computation costs caused by the need for each test sample to find the distance between it and each training sample.
- The storage requirement is large since the entire dataset needs to be stored in memory.
- Outliers can negatively affect the accuracy of the classifier.
- The negative effect of data sets with non-separable and/or overlapping classes.
- The low tolerance to noise.
CLIFF takes a dataset and for each class ranks (power) each attribute sub-range using BORE. Multiply ranks of each row then select the most powerful rows of each class.
PrivacyNow I use CLIFF along with MORPH to privatized defect datasets. CLIFF removes the overlap of classes with instance reduction while MORPH moves remaining data to low density areas and avoids overlap.
y = x ± (x − z) ∗ r
- x ∈ D, the original instance;
- z ∈ D the NUN of x;
- y the resulting MORPHed instance.