کد متلب کنترل فازی مقاوم غیر خطی PCA
A New Nonlinear Fuzzy Robust PCA Algorithm and Similarity Classifier in
Classification of Medical Data Sets
چکیده مقاله
Abstract1
In this article a classification method is proposed
where data is first preprocessed using new nonlinear
fuzzy robust principal component analysis (NFRPCA)
algorithm to get data into more feasible form. After
this preprocessing step the similarity classifier is then
used for the actual classification. The procedure was
tested for dermatology, hepatitis and liver-disorder
data. Results were quite promising and better classification accuracy was achieved than using classical
PCA and similarity classifier. This new nonlinear
fuzzy robust principal component analysis algorithm
seems to have the effect that it project the data sets
into a more feasible form and when used together with
the similarity classifier a classification accuracy of
72.27 % was achieved with liver-disorder data, 88.94
% with hepatitis, and 97.09 % accuracy was achieved
with dermatology data. Compared to results with
classical PCA and the similarity classifier, higher accuracies were achieved with the approach using
nonlinear fuzzy robust principal component analysis
and the similarity classifier
Principal component analysis (PCA) [2] is a well extablished technique for data analysis and preprocessing.
The general motivation for PCA is dimension reduction.
PCA decomposes high dimensional data into a low dimensional subspace component and a noise component.
Nowadays, dimensionality reduction techniques such as
PCA are often used before classification [3, 4, 5] Many
databases that come from the real world are coupled with
noise, a random error or variance of a measured variable
[6]. Thus, real world data analysis is almost always burdened with uncertainty of different kinds.
Keywords: Dimension reduction, Nonlinear Fuzzy
robust PCA, Medical data, Similarity classifier
.
لینک مقاله در sciencedirect
http://www.sciencedirect.com/science/article/pii/S0957417410004665
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