An Analysis Of Two Dimensionality Reduction Techniques On The Performance Of Neural Network Classifiers
This project involves an analysis of the effectiveness of two dimensionality reduction techniques, i.e., Principal Component Analysis as the standard approach and Random Projection as a recent technique. The study is based on the performance of two supervised neural network classifiers i.e., S...
Saved in:
| Main Author: | |
|---|---|
| Format: | Monograph |
| Language: | English |
| Published: |
Universiti Sains Malaysia
2005
|
| Subjects: | |
| Online Access: | http://eprints.usm.my/57588/1/An%20Analysis%20Of%20Two%20Dimensionality%20Reduction%20Techniques%20On%20The%20Performance%20Of%20Neural%20Network%20Classifiers_Ong%20Siok%20Lan.pdf http://eprints.usm.my/57588/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | This project involves an analysis of the effectiveness of two dimensionality reduction
techniques, i.e., Principal Component Analysis as the standard approach and Random
Projection as a recent technique. The study is based on the performance of two supervised
neural network classifiers i.e., Standard Backpropagation and Fuzzy ARTMAP. A set of
benchmark and real medical databases are used to evaluate the performance of the neural
network models. The performance indicators used are percentage of correct classification,
purity, and collective entropy. The Student’s two-tailed paired t-test is used to compare the
significance of differences of the results. Based on the estimated 95% confidence intervals,
a strong decision which eventually leads to a convincing conclusion on the performance of
the dimensionality reduction techniques can be obtained. The perceived experimental
results especially from the real medical data sets are encouraging enough to prove that
Random Projection exhibits good performance as a dimensionality reduction technique.
Surprisingly, Random Projection is effective on low dimensional data, and the outcomes
are as good as Principal Component Analysis. A discussion on generalization of the results
obtained is included, and a conclusion ensues. Recommendations are also included for
further improvements and enhancements in the analysis of dimensionality reduction
techniques. |
|---|