Scaling of Cloud Resources-Principal Component Analysis and Random Forest Approach

Abstract Background Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential. Results Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611. Conclusions We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.

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Classification of Sport Actions Using Principal Component Analysis and Random Forest Based on Three-Dimensional Data

Displays ◽

10.1016/j.displa.2021.102135 ◽

2021 ◽

pp. 102135

Author(s):

Hao Weiwei

Keyword(s):

Principal Component Analysis ◽

Random Forest ◽

Three Dimensional ◽

Principal Component ◽

Component Analysis

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Fault diagnosis approach of traction transformers in high‐speed railway combining kernel principal component analysis with random forest

IET Electrical Systems in Transportation ◽

10.1049/iet-est.2015.0018 ◽

2016 ◽

Vol 6 (3) ◽

pp. 202-206 ◽

Cited By ~ 17

Author(s):

Chenxi Dai ◽

Zhigang Liu ◽

Keting Hu ◽

Ke Huang

Keyword(s):

Principal Component Analysis ◽

Fault Diagnosis ◽

Random Forest ◽

High Speed ◽

Principal Component ◽

Component Analysis ◽

Kernel Principal Component Analysis ◽

High Speed Railway ◽

Diagnosis Approach

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Towards a software defect proneness model: feature selection

Applied Aspects of Information Technology ◽

10.15276/aait.04.2021.5 ◽

2021 ◽

Vol 4 (4) ◽

pp. 354-365

Author(s):

Vitaliy S. Yakovyna ◽

◽

Ivan I. Symets

Keyword(s):

Principal Component Analysis ◽

Feature Selection ◽

Random Forest ◽

Software Reliability ◽

Principal Component ◽

Component Analysis ◽

Support Vector ◽

Tree Classifier ◽

Code Metrics ◽

Software Code

This article is focused on improving static models of software reliability based on using machine learning methods to select the software code metrics that most strongly affect its reliability. The study used a merged dataset from the PROMISE Software Engineering repository, which contained data on testing software modules of five programs and twenty-one code metrics. For the prepared sampling, the most important features that affect the quality of software code have been selected using the following methods of feature selection: Boruta, Stepwise selection, Exhaustive Feature Selection, Random Forest Importance, LightGBM Importance, Genetic Algorithms, Principal Component Analysis, Xverse python. Basing on the voting on the results of the work of the methods of feature selection, a static (deterministic) model of software reliability has been built, which establishes the relationship between the probability of a defect in the software module and the metrics of its code. It has been shown that this model includes such code metrics as branch count of a program, McCabe’s lines of code and cyclomatic complexity, Halstead’s total number of operators and operands, intelligence, volume, and effort value. A comparison of the effectiveness of different methods of feature selection has been put into practice, in particular, a study of the effect of the method of feature selection on the accuracy of classification using the following classifiers: Random Forest, Support Vector Machine, k-Nearest Neighbors, Decision Tree classifier, AdaBoost classifier, Gradient Boosting for classification. It has been shown that the use of any method of feature selection increases the accuracy of classification by at least ten percent compared to the original dataset, which confirms the importance of this procedure for predicting software defects based on metric datasets that contain a significant number of highly correlated software code metrics. It has been found that the best accuracy of the forecast for most classifiers was reached using a set of features obtained from the proposed static model of software reliability. In addition, it has been shown that it is also possible to use separate methods, such as Autoencoder, Exhaustive Feature Selection and Principal Component Analysis with an insignificant loss of classification and prediction accuracy

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