Principal Component Feature Extraction Research Articles

Prediction of liver diseases with Random Forest classifier with principal component feature extraction

Liver disease is one of the most major illnesses that has a negative influence on the normal, healthy stature of a human being. This is because many different factors may lead to liver disease. The liver is the organ that is most often afflicted by liver disease even though it is the largest organ found inside the body. A few examples of the various subtypes of liver illness include fatty liver disease, cirrhosis, hepatitis, chronic liver disease, liver cancer, liver tumors, and other kinds of liver disease. This study creates machine learning algorithms to enhance the prediction of liver disease using various data balancing techniques, such as the Synthetic Minority Oversampling Technique (SMOTE) with Edited Nearest Neighbourhood (ENN). The traits that were derived from SMOTE-ENN balanced features are then normalised using the principal component analysis (PCA) method. In addition, approaches like as correlation and skewness are used to clean the dataset and minimize the number of features. Finally, the prediction operation is carried out by a Random Forest classifier with PCA extracted and SEMOTE-ENN balanced features. The simulations conducted liver-disease dataset show that the proposed method results in improved performance over existing methods.

A detection method of typical toxic mixed red tide algae in Qinhuangdao based on three-dimensional fluorescence spectroscopy

Red tides occur every year in the Qinhuangdao sea area of China, including a variety of toxic algae and non-toxic algae. Toxic red tide algae have caused great damage to the marine aquaculture industry in China and seriously endangered human health, but most of non-toxic algae are important bait for marine plankton. Therefore, it is very important to identify the type of mixed red tide algae in Qinhuangdao sea area. In this paper, three-dimensional fluorescence spectroscopy and chemometrics were applied to the identification of typical toxic mixed red tide algae in Qinhuangdao. Firstly, the three-dimensional fluorescence spectrum data of typical mixed red tide algae in Qinhuangdao sea area were measured by f-7000 fluorescence spectrometer, and the contour map of algae samples was obtained. Secondly, the contour spectrum analysis is carried out to find the excitation wavelength of the peak position of the three-dimensional fluorescence spectrum and form the new three-dimensional fluorescence spectrum data selected by the feature interval. Then, the new three-dimensional fluorescence spectrum data are extracted by principal component analysis (PCA). Finally, the feature extraction data and the data without feature extraction are used as the input of the genetic optimization support vector machine (GA-SVM) and particle swarm optimization support vector machine (PSO-SVM) classification models, respectively, to obtain the classification model of mixed red tide algae, and the two feature extraction analysis methods and two classification algorithms are compared. The results show that the classification accuracy of the test set using the principal component feature extraction and GA-SVM classification method is 92.97 %, when the excitation wavelengths are 420 nm, 440 nm, 480 nm, 500 nm and 580 nm, and the emission wavelengths are 650–750 nm. Therefore, it is feasible and effective to apply the three-dimensional fluorescence spectrum characteristics and genetic optimization support vector machine classification method to the identification of toxic mixed red tide algae in Qinhuangdao sea area.

State of health estimation for lithium-ion battery based on the coupling-loop nonlinear autoregressive with exogenous inputs neural network

In order to solve the problem of low accuracy of traditional artificial neural networks in approximating functional, a coupling-loop nonlinear autoregressive with exogenous inputs neural network estimation model is proposed and applied to the state of health forecasting of lithium-ion batteries. Firstly, eight health indicators related to the state of health of lithium-ion batteries are mined. In order to eliminate the harm of weakly correlated independent variables and redundant independent variables to the estimation results, principal component feature extraction is applied to the feature extraction of the eight health indicators. Bayesian regularization algorithm is used to learn the weights of the neural network, which solves the problems of slow convergence speed and easy to fall into local extremum of back propagation learning algorithm, and improves the generalization ability of the neural network. In order to verify the rationality of the proposed model and algorithm, the coupling-loop nonlinear autoregressive with exogenous inputs neural network model is used to estimate the state of health of lithium battery under the condition of complete charge discharge test, and the estimation results are compared with other neural network models. The simulation results show that the coupling-loop nonlinear autoregressive with exogenous inputs estimation model using feature extraction method and Bayesian network weight algorithm can approach the functional with higher accuracy, and the absolute error and relative error of lithium-ion battery's state of health estimation can be reduced by more than 50% on average, while the mean square error can be reduced by more than 80%.

Clustering analysis of the distribution characteristics of unobserved economic regions in China——Based on multi-indicator panel data

The existence of unobserved economy is one of the important factors affecting GDP calculation. This paper uses the provincial panel data from 2010 to 2019 in China, and adopts the method of principal component feature extraction to carry out cluster analysis on the multi-indicator panel data. This method preserves the dynamic characteristics of the panel data, calculates the comprehensive score of each eigenvalue, and gives weight to the eigenvalue by using the entropy method, so as to optimize the clustering results representing the eight indicators of the unobserved economy. Through the analysis, it is found that the regional development of China’s unobserved economy is obviously different, and each type has different influencing factors. This result has important practical significance for different regions in China to formulate differentiated unobserved economic governance policies. This also helps to make better use of resources and develop an energy-saving economy.

A Method for Monitoring the Spatial Distribution of Nutrients from the Perspective of Landscape Ecology

Zhang, W., 2020. A method for monitoring the spatial distribution of nutrients from the perspective of landscape ecology. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 182–186. Coconut Creek (Florida), ISSN 0749-0208.In order to quantify the spatial distribution characteristics of nutrient salts and monitor the ecological landscape characteristics of the spatial distribution of nutrients, a monitoring model based on fuzzy set theory for spatial distribution of ecological landscape characteristics of nutrient salts is proposed, in which the spatial distribution of nutrient salts is used to cluster the ecological landscape. The fuzzy constraint parameter model of ecological landscape feature monitoring of spatial distribution of nutrient salts is constructed based on the constraint index set, such as beauty, landscape natural fusion and energy saving and environmental protection, and so on. The analytic hierarchy process (AHP) is used to construct the statistical analysis model of spatial distribution monitoring of nutrient salts from the perspective of landscape ecology, and the fuzzy set theory is used to design the optimal control model of the monitoring model of spatial distribution characteristics of nutrient salts. The ecological landscape feature monitoring problem of nutrient spatial distribution is transformed into the binary programming problem of seeking fuzzy set. The spatial distribution characteristic map analysis and principal component feature extraction method of nutrient salt are adopted. The spatial distribution of nutrient salts in landscape is optimized to monitor the spatial distribution characteristics of nutrient salts. The results of empirical analysis show that the monitoring of ecological landscape characteristics of spatial distribution of nutrients is more accurate, more stable and more efficient.

Bayesian Discriminant Analysis of Yogurt Products Based on Raman Spectroscopy.

The quality discrimination of dairy products is an important basis on which to achieve quality assurance. Taking the discriminant analysis of brand yogurt products as an example, a new rapid discriminant method can be constructed. The first three principal components were selected as the pattern vectors of the samples. Then, at random, 75% of the samples were collected as a training set, and their mean values and covariance matrices were calculated to construct a Gauss Bayesian discriminant model. The remaining 25% of samples were employed as a test set, and the pattern vectors of each sample were input into the above model. Next, the posterior probability of each sample in relation to each category could be obtained. Results: The category corresponding to the maximum posterior probability as the brand classification of each sample was defined. We constructed a Gauss Bayesian discriminant model to discriminate these different yogurt products after the principal component feature extraction of Raman properties. The results indicate the rationality and wide application prospects of this approach. A fast dairy product discriminant method based on Gauss Bayesian model and Raman spectroscopy was established.

A novel method for spacecraft electrical fault detection based on FCM clustering and WPSVM classification with PCA feature extraction

The measurement of spacecraft electrical characteristics and multi-label classification issues are generally including a large amount of unlabeled test data processing, high-dimensional feature redundancy, time-consumed computation, and identification of slow rate. In this paper, a fuzzy c-means offline (FCM) clustering algorithm and the approximate weighted proximal support vector machine (WPSVM) online recognition approach have been proposed to reduce the feature size and improve the speed of classification of electrical characteristics in the spacecraft. In addition, the main component analysis for the complex signals based on the principal component feature extraction is used for the feature selection process. The data capture contribution approach by using thresholds is furthermore applied to resolve the selection problem of the principal component analysis (PCA), which effectively guarantees the validity and consistency of the data. Experimental results indicate that the proposed approach in this paper can obtain better fault diagnosis results of the spacecraft electrical characteristics’ data, improve the accuracy of identification, and shorten the computing time with high efficiency.

A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM.

This paper proposes a novel multi-label classification method for resolving the spacecraft electrical characteristics problems which involve many unlabeled test data processing, high-dimensional features, long computing time and identification of slow rate. Firstly, both the fuzzy c-means (FCM) offline clustering and the principal component feature extraction algorithms are applied for the feature selection process. Secondly, the approximate weighted proximal support vector machine (WPSVM) online classification algorithms is used to reduce the feature dimension and further improve the rate of recognition for electrical characteristics spacecraft. Finally, the data capture contribution method by using thresholds is proposed to guarantee the validity and consistency of the data selection. The experimental results indicate that the method proposed can obtain better data features of the spacecraft electrical characteristics, improve the accuracy of identification and shorten the computing time effectively.