scholarly journals Classification Performance Using Principal Component Analysis and Different Value of the Ratio R

2011 ◽  
Vol 6 (2) ◽  
pp. 317 ◽  
Author(s):  
Jasmina Novakovic ◽  
Sinisa Rankov
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Classification Performance ◽  
Classification Algorithms ◽  
Data Sets ◽  
Best Value ◽  
Inherent Feature ◽  
Different Types

A comparison between several classification algorithms with feature extraction on real dataset is presented. Principal Component Analysis (PCA) has been used for feature extraction with different values of the ratio R, evaluated and compared using four different types of classifiers on two real benchmark data sets. Accuracy of the classifiers is influenced by the choice of different values of the ratio R. There is no best value of the ratio R, for different datasets and different classifiers accuracy curves as a function of the number of features used may significantly differ. In our cases feature extraction is especially effective for classification algorithms that do not have any inherent feature selections or feature extraction build in, such as the nearest neighbour methods or some types of neural networks.

scholarly journals Effects of Collagen–Glycosaminoglycan Mesh on Gene Expression as Determined by Using Principal Component Analysis-Based Unsupervised Feature Extraction

Polymers ◽  
2021 ◽  
Vol 13 (23) ◽  
pp. 4117
Author(s):  
Y-h. Taguchi ◽  
Turki Turki
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Feature Extraction ◽  
Cell Lines ◽  
Time Course ◽  
Expression Profiles ◽  
Principal Component ◽  
Component Analysis ◽  
Altered Expression ◽  
Unsupervised Feature Extraction

The development of the medical applications for substances or materials that contact cells is important. Hence, it is necessary to elucidate how substances that surround cells affect gene expression during incubation. In the current study, we compared the gene expression profiles of cell lines that were in contact with collagen–glycosaminoglycan mesh and control cells. Principal component analysis-based unsupervised feature extraction was applied to identify genes with altered expression during incubation in the treated cell lines but not in the controls. The identified genes were enriched in various biological terms. Our method also outperformed a conventional methodology, namely, gene selection based on linear regression with time course.

Design and Development of EMG Conditioning System and Hand Gesture Recognition Based on Principal Component Analysis Feature Reduction Technique

2014 ◽  
pp. 304-320
Author(s):  
P. Geethanjali
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Digital Signal ◽  
Component Analysis ◽  
Classification Performance ◽  
Feature Reduction ◽  
Design And Development ◽  
Emg Signal ◽  
Knn Classifier ◽  
Power Line Interference

This chapter discusses design and development of a surface Electromyogram (EMG) signal detection and conditioning system along with the issues of gratuitous spurious signals such as power line interference, artifacts, etc., which make signals plausible. In order to construe the recognition of hand gestures from EMG signals, Time Domain (TD) and well as Autoregressive (AR) coefficients features are extracted. The extracted features are diminished using the Principal Component Analysis (PCA) to alleviate the burden of the classifier. A four-channel continuous EMG signal conditioning system is developed and EMG signals are acquired from 10 able-bodied subjects to classify the 6 unique movements of hand and wrist. The reduced statistical TD and AR features are used to classify the signal patterns through k Nearest Neighbour (kNN) as well as Neural Network (NN) classifier. Further, EMG signals acquired from a transradial amputee using 8-channel systems for the 6 amenable motions are also classified. Statistical Analysis of Variance (ANOVA) results on classification performance of able-bodied subject divulge that the performance TD-PCA features are more significant than the AR-PCA features. Further, no significant difference in the performance of NN classifier and kNN classifier is construed with TD reduced features. Since the average classification error of kNN classifier with TD features is found to be less, kNN classifier is implemented in off-line using the TMS2407eZdsp digital signal controller to study the actuation of three low-power DC drives in the identification of intended motion with an able-bodied subject.

Heuristic Principal Component Analysis-Based Unsupervised Feature Extraction and Its Application to Bioinformatics

2015 ◽  
pp. 138-162 ◽  
Author(s):  
Y-H. Taguchi ◽  
Mitsuo Iwadate ◽  
Hideaki Umeyama ◽  
Yoshiki Murakami ◽  
Akira Okamoto
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Circulating Microrna ◽  
Biomarker Identification ◽  
Typical Situation ◽  
Aberrant Dna Methylation ◽  
The Stability ◽  
Unsupervised Feature Extraction

Feature Extraction (FE) is a difficult task when the number of features is much larger than the number of samples, although that is a typical situation when biological (big) data is analyzed. This is especially true when FE is stable, independent of the samples considered (stable FE), and is often required. However, the stability of FE has not been considered seriously. In this chapter, the authors demonstrate that Principal Component Analysis (PCA)-based unsupervised FE functions as stable FE. Three bioinformatics applications of PCA-based unsupervised FE—detection of aberrant DNA methylation associated with diseases, biomarker identification using circulating microRNA, and proteomic analysis of bacterial culturing processes—are discussed.

Principal Component Analysis of Hydrological Data

2010 ◽  
pp. 364-388
Author(s):  
Petr Praus
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Drinking Water ◽  
Ground Water ◽  
Principal Component ◽  
Component Analysis ◽  
Data Sets ◽  
Hydrological Data ◽  
First Case

In this chapter the principals and applications of principal component analysis (PCA) applied on hydrological data are presented. Four case studies showed the possibility of PCA to obtain information about wastewater treatment process, drinking water quality in a city network and to find similarities in the data sets of ground water quality results and water-related images. In the first case study, the composition of raw and cleaned wastewater was characterised and its temporal changes were displayed. In the second case study, drinking water samples were divided into clusters in consistency with their sampling localities. In the case study III, the similar samples of ground water were recognised by the calculation of cosine similarity, the Euclidean and Manhattan distances. In the case study IV, 32 water-related images were transformed into a large image matrix whose dimensionality was reduced by PCA. The images were clustered using the PCA scatter plots.

Applying Weighted PCA on Multiclass Classification for Intrusion Detection

2011 ◽  
pp. 220-241
Author(s):  
Mohsen Moshki ◽  
Mehran Garmehi ◽  
Peyman Kabiri
Keyword(s):  
Principal Component Analysis ◽  
Feature Extraction ◽  
Intrusion Detection ◽  
Principal Component ◽  
Component Analysis ◽  
Multiclass Classification ◽  
Extended Version ◽  
Number Of Classes

In this chapter, application of Principal Component Analysis (PCA) and one of its extensions on intrusion detection is investigated. This extended version of PCA is modified to cover an important shortcoming of traditional PCA. In order to evaluate these modifications, it is mathematically proved that these modifications are beneficial and later on a known dataset such as the DARPA99 dataset is used to verify results experimentally. To verify this approach, initially the traditional PCA is used to preprocess the dataset. Later on, using a simple classifier such as KNN, the effectiveness of the multiclass classification is studied. In the reported work, instead of traditional PCA, a revised version of PCA named Weighted PCA (WPCA) will be used for feature extraction. The results from applying the aforementioned method to the DARPA99 dataset show that this approach results in better accuracy than the traditional PCA when a number of features are limited, a number of classes are large, and a population of classes is unbalanced. In some situations WPCA outperforms traditional PCA by more than 1% in accuracy.

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