Feature Selection in Gene Expression Data Using Principal Component Analysis and Rough Set Theory

Abstract

In many fields such as data mining, machine learning, pattern recognition and signal processing, data sets containing huge number of features are often involved. Feature selection is an essential data preprocessing technique for such high-dimensional data classification tasks. Traditional dimensionality reduction approach falls into two categories: Feature Extraction (FE) and Feature Selection (FS). Principal component analysis is an unsupervised linear FE method for projecting high-dimensional data into a low-dimensional space with minimum loss of information. It discovers the directions of maximal variances in the data. The Rough set approach to feature selection is used to discover the data dependencies and reduction in the number of attributes contained in a data set using the data alone, requiring no additional information. For selecting discriminative features from principal components, the Rough set theory can be applied jointly with PCA, which guarantees that the selected principal components will be the most adequate for classification. We call this method Rough PCA. The proposed method is successfully applied for choosing the principal features and then applying the Upper and Lower Approximations to find the reduced set of features from a gene expression data.

Gene expression network discovery

“…First, initially the proposed model by Dash et al [20] and Mishra et al [21] has been evaluated on a synthetic dataset with 15 data objects having 10 attributes. Second, we have evaluated the MSR approach of Cheng & Church [2] and pattern based approach [1] to find the coherent patterns from the synthetic data set as well as Yeast data set.…”

“…Principal Component Analysis (PCA) [15][16][20] [21] is an unsupervised linear feature reduction method for projecting high dimensional data into a new low dimensional representation of the data that describes as much of the variance in the data as possible with minimum loss of information. PCA is mathematically defined as an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on.…”

A New Avenue for Classification and Prediction of Olive Cultivars Using Supervised and Unsupervised Algorithms

“…More than 20% of the attribute alleles discarded when these algorithms were applied on the original dataset. Each attribute weighting system uses a specific pattern to define the most important features by feature selection [37]–[39]. Thus, the results may be different [40], as has been highlighted in previous studies [13]–[17].…”

A Hybrid Kinematic-Acoustic System for Automated Activity Detection of Construction Equipment

“…They have reported that PCA can decrease the computational time significantly, which can further be used for real-time purposes. Before using PCA, the extracted features are normalized using z-score to avoid scaling effects, while ensuring that any feature with larger domain will not dominate features with smaller domain [64,65,66]. A feature value X of a feature F is normalized to X′ using Equation (3):X′=X−μ(F)σ(F) where, X is the feature value, μ(F) is the arithmetic mean of all values of feature F, σ(F) is the standard deviation of all values of feature F, and X′ is the normalized feature value.…”