Improved scalability and risk factor proxying with a two-step principal component analysis for multi-curve modelling

Atkins, Philip J.; Cummins, Mark

doi:10.1016/j.ejor.2022.04.044

Cited by 4 publications

(5 citation statements)

References 28 publications

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“…The PC data is a linear combination of the original values before being reduced. The PC are obtained by projecting the vector into the space defined by the eigenvectors with some calculations, namely: i) calculate the covariance matrix; ii) find the eigenvalues and eigenvectors; iii) compute reduction percentage; and iv) PC [8], [24], [25].…”

Section: Principal Component Analysismentioning

confidence: 99%

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Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

Nurcahyawati¹,

Mustaffa

2023

Bulletin EEI

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High dimensionality in data sets is one of the challenges faced in classification, data mining, and sentiment analysis. In the data set, many dimensionalities require effort to simplify. Many of these dimensionalities have a major impact on the complexity and performance of the algorithms used for classification. Various challenges were encountered, including how to determine the optimal combination of pre-processing techniques, how to clean the dataset, and determine the best classification algorithm. This study uses a new approach based on the combination of three powerful techniques which are: tokenizing-lowercasing-stemming (for series of preprocessing), support vector machine (SVM) for supervised classification, and fuzzy matching (FM) for dimensionality reduction. The proposed model was realized using 3 different datasets, namely Amazon product review, movie review, and airline review from Twitter. This study provides better findings than the previous results. Improved performance is generated by SVM combined with FM, resulting in 96% accuracy. So that the SVM-FM combination can be said to be the best combination for sentiment analysis on the given data set.

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Section: Principal Component Analysismentioning

confidence: 99%

“…According to Romadhon and Kurniawan [17] apply kNN and produce 75% accuracy. To simplify the dimensions of the analysed text, an algorithm is used FM [3], PCA [8], [24], [25], EDS [7].…”

Section: Introductionmentioning

confidence: 99%

Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

Nurcahyawati¹,

Mustaffa

2023

Bulletin EEI

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“…1) Standardize the data of yield risk factors. The risk factor data of the original yield is standardized to eliminate the impact of the yield risk factor data dimension and order of magnitude [17]. The standardization equation is:…”

Section: A the Dimensionality Reduction Methods Of Risk Factormentioning

confidence: 99%

Construction of an Intelligent Evaluation Model of Yield Risk Based on Empirical Probability Distribution

Yanru,

Jing

2023

IJACSA

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In order to improve the accuracy of yield risk evaluation, an intelligent evaluation model of yield risk based on empirical probability distribution is constructed. The dimensionality reduction method of risk factor based on principal component analysis is adopted. After adjusting the multiple data dimensions of risk factors that affect the rate of return to a unified dimension, the cluster-based evaluation index screening method is used to build the evaluation index set that best reflects the risk of the rate of return; The index weight vector equation method based on entropy weight and information entropy is used to set the evaluation index weight. Through the comprehensive evaluation model based on the empirical probability distribution of risk indicators, the empirical probability distribution information of risk indicators at all levels is analyzed, and the risk level of yield is intelligently evaluated. The research structure shows that the model can effectively evaluate the level of return risk and provide an effective reference for preventing and controlling investment return risk.

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“…The principal as the leader of education in the school has an important role in improving the quality of the learning process in the school he leads (Atkins & Cummins, 2022). The school principal, who is the sole leader in the school, has the responsibility to teach and influence all parties involved in educational activities at school to want to work together in achieving school goals (Castro-rodriguez et al, 2015).…”

Section: Aspects Innovatormentioning

confidence: 99%

“…In the last decade, various studies oriented towards improving the quality of education in Vocational High Schools have been carried out (e.g. Atkins & Cummins, 2022;Lisnawati, 2017;Maryono, 2015;Ramos-pla et al, 2021;Zubair & Sasongko, 2021), but no research has been found on the management of school principals in improving the quality of education in Vocational High Schools. So that this research focuses on the management of school principals in improving the quality of education.…”

Section: Introductionmentioning

confidence: 99%

Principal's Managerial Performance to Improve Quality of Education at Vocational High School

Sutikno

2023

Scaffolding

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This study discusses the importance of principal management in efforts to improve the quality of education in vocational high schools, given the importance of the role of school principals where all policies are oriented towards improving the quality of education. Based on these objectives, a research design was then chosen using a quantitative design. The subject used in this study consisted of three groups, namely vice principals, education staff, and students. The instrument used was a questionnaire used to measure the managerial skills of school principals which consisted of three aspects including the innovator aspect consisting of four indicators, the motivator consisting of seven indicators, and the supervisor consisting of three indicators. The empirical methodology used in practical descriptive analysis represents trends and variations in data such as mean, median, and mode), which are very useful for explaining research data. Descriptive statistical analysis assisted by the SPSS program. The results of the study found that school principals had been able to improve the quality of education in vocational schools based on the innovator aspect with the highest average of 29.40 (Std. Dev 20.28), motivators with the highest average of 49.00 (Std. Dev 38.33), and supervisors with the highest average of 49.00 (Std. Dev 37.59).

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Improved scalability and risk factor proxying with a two-step principal component analysis for multi-curve modelling

Cited by 4 publications

References 28 publications

Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

Improving sentiment reviews classification performance using support vector machine-fuzzy matching algorithm

Construction of an Intelligent Evaluation Model of Yield Risk Based on Empirical Probability Distribution

Principal's Managerial Performance to Improve Quality of Education at Vocational High School

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