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Selecting subsets of newly extracted features from PCA and PLS in microarray data analysis.

Guo-Zheng Li¹, Hua-Long Bu, Mary Qu Yang

¹Department of Control Science & Engineering, Tongji University, Shanghai 201804, PR China. drgzli@gmail.com

|October 10, 2008

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Selecting features from all components, not just the top ones, improves classifier performance on high-dimensional gene expression data. This approach enhances generalization by using feature selection on Principle Component Analysis (PCA) or Partial Least Squares (PLS) components.

Area of Science:

Bioinformatics
Computational Biology
Machine Learning

Background:

High-dimensional gene expression microarray data presents challenges for classifier generalization.
Dimension reduction techniques, including feature selection and extraction, are crucial for analysis.
Principle Component Analysis (PCA) and Partial Least Squares (PLS) are common feature extraction methods.

Purpose of the Study:

To develop and demonstrate a framework for selecting optimal feature subsets from all extracted components.
To improve the generalization performance of classifiers in gene expression data analysis.
To challenge the conventional approach of selecting only top-ranked components.

Main Methods:

Utilized unsupervised PCA and supervised PLS for component extraction.

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Employed genetic algorithms for feature selection across all extracted components.

Applied Support Vector Machines (SVM) and k-Nearest Neighbor (kNN) for classification tasks.

Main Results:

The proposed framework effectively selects relevant feature subsets from all components.
Demonstrated reduced classification error rates on gene expression microarray data.
Experimental results confirm the framework's efficacy in improving classifier performance.

Conclusions:

Feature selection from all extracted components, not just the top ones, is essential.
This approach enhances classifier generalization performance in microarray data analysis.
The study highlights the importance of comprehensive feature selection for robust classification.