A Novel Feature Selection and Short-Term Price Forecasting Based on a Decision Tree (J48) Model

Srivastava, Ankit; Singh, Devender; Pandey, Ajay Shekhar; Maini, Tarun

doi:10.3390/en12193665

Cited by 11 publications

(7 citation statements)

References 33 publications

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“…The usefulness of the approach is established through the performance test of the forecaster using the feature selected by this approach. It is found out that the forecast with the reduced features consistently outperformed a more extensive feature set [43].…”

Section: Feature Selection-based Classification Algorithmsmentioning

confidence: 94%

Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

Gamao¹

2019

IJATCSE

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Academic database is considered as the heart and soul of every higher education institutions. This database contains a vast amount of useful information that is useful for analysis. Algorithms for machine learning play a significant role in mining academic databases and have been proven to be effective when applied in the academic field. Prediction models are made using relevant classification algorithms for dropout analysis. The success of the prediction model depends on the performance of the feature selection algorithm used for dimensionality reduction. The study utilized the Modified Mutated Firefly Algorithm (MMFA) as a dimensionality reduction strategy to enhance the accuracy of the prediction model for dropout analysis. The results of the simulation revealed that the Decision Tree (DT) classifier outperformed the Naïve Bayesian using the three UCI datasets. After the test of benchmark datasets, a students' cumulative dataset was used to come up with a predictive model for dropout analysis of Davao del Norte State College, Davao del Norte, Philippines. The results of the experiment confirmed that the MMFA+DT obtained an accuracy rate of 95.82%, while MMFA+NB only has 92.85% using 10-fold cross-validation.

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Section: Feature Selection-based Classification Algorithmsmentioning

confidence: 94%

Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

Gamao¹

2019

IJATCSE

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“…Ciabattoni et al (2015) proposed a univariate filter method based on the Bayes error rate for feature selection in fault detection. Srivastava et al (2019) proposed a novel feature selection method based on a price prediction decision tree. This method used genetic algorithms and decision tree classifiers to obtain the smallest number of features for the best prediction accuracy.…”

Section: Feature Selectionmentioning

confidence: 99%

Application of Decision Tree and Relative Proximity to Evaluation of Sustainable Development Capacities of Listed Electric Power Companies

Mou

Tang

Hang

2022

Journal of Business Economics and Management

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In a complex market environment with fierce competition, maintaining their current market positions is an important issue for electric power companies. Sustainable development not only requires them to pay attention to their current operating efficiencies but also to actively participate in environmental and social responsibilities to maintain their competitive advantages. This paper proposes a model for evaluating the sustainable development capacities of power companies. Firstly, the preliminary evaluation indicator system is constructed with the seven dimensions of production safety, public relations and social welfare, shareholder rights protection, environmental sustainability, employee rights protection, scientific research innovation ability, and financial status. Then, specific financial indicators are selected by CART to avoid indicator redundancies and the final evaluation indicator system is constructed. Finally, the relative proximity calculated by the TOPSIS method is applied to evaluate the sustainable development capacities. An empirical study of 18 listed electric power companies is conducted to verify the evaluation model. The results show that the performances of these companies in production safety and environmental sustainability are generally satisfactory, but the overall performances in public relations and social welfare, employee rights protection, and scientific research innovation ability are relatively poor, so these dimensions should be strengthened.

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“…In step 5, based on the value of the gain ratio, the attribute having the highest value is declared root node, and the same computation is repeated from step 1 to step 4 for intermediate nodes till all the instances are exhausted and reach the leaf node as per step 2 [33].…”

Section: J48 Classifiermentioning

confidence: 99%

A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

et al. 2021

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Lack of education is a major concern in underdeveloped countries because it leads to poor human and economic development. The level of education in public institutions varies across all regions around the globe. Current disparities in access to education worldwide are mostly due to systemic regional differences and the distribution of resources. Previous research focused on evaluating students’ academic performance, but less has been done to measure the performance of educational institutions. Key performance indicators for the evaluation of institutional performance differ from student performance indicators. There is a dire need to evaluate educational institutions’ performance based on their disparities and academic results on a large scale. This study proposes a model to measure institutional performance based on key performance indicators through data mining techniques. Various feature selection methods were used to extract the key performance indicators. Several machine learning models, namely, J48 decision tree, support vector machines, random forest, rotation forest, and artificial neural networks were employed to build an efficient model. The results of the study were based on different factors, i.e., the number of schools in a specific region, teachers, school locations, enrolment, and availability of necessary facilities that contribute to school performance. It was also observed that urban regions performed well compared to rural regions due to the improved availability of educational facilities and resources. The results showed that artificial neural networks outperformed other models and achieved an accuracy of 82.9% when the relief-F based feature selection method was used. This study will help support efforts in governance for performance monitoring, policy formulation, target-setting, evaluation, and reform to address the issues and challenges in education worldwide.

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A Novel Feature Selection and Short-Term Price Forecasting Based on a Decision Tree (J48) Model

Cited by 11 publications

References 33 publications

Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

Prediction-Based Model for Student Dropouts using Modified Mutated Firefly Algorithm

Application of Decision Tree and Relative Proximity to Evaluation of Sustainable Development Capacities of Listed Electric Power Companies

A Novel Method for Performance Measurement of Public Educational Institutions Using Machine Learning Models

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