Clarify of the Random Forest Algorithm in an Educational Field

Ahmed, Nawzat S.; Sadiq, Mohammed Hikmat

doi:10.1109/icoase.2018.8548804

Cited by 24 publications

(10 citation statements)

References 7 publications

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“…Random forest (RF) [40] represents an ensemble learning method that accomplishes classification by constructing numerous decision trees and combining their outcomes. This widely used machine learning algorithm harnesses the diversity and collective knowledge of multiple decision trees to enhance prediction accuracy and robustness.…”

Section: Classifiersmentioning

confidence: 99%

Semi-Supervised Feature Selection of Educational Data Mining for Student Performance Analysis

Yu,

Cai,

Pan

et al. 2024

Electronics

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In recent years, the informatization of the educational system has caused a substantial increase in educational data. Educational data mining can assist in identifying the factors influencing students’ performance. However, two challenges have arisen in the field of educational data mining: (1) How to handle the abundance of unlabeled data? (2) How to identify the most crucial characteristics that impact student performance? In this paper, a semi-supervised feature selection framework is proposed to analyze the factors influencing student performance. The proposed method is semi-supervised, enabling the processing of a considerable amount of unlabeled data with only a few labeled instances. Additionally, by solving a feature selection matrix, the weights of each feature can be determined, to rank their importance. Furthermore, various commonly used classifiers are employed to assess the performance of the proposed feature selection method. Extensive experiments demonstrate the superiority of the proposed semi-supervised feature selection approach. The experiments indicate that behavioral characteristics are significant for student performance, and the proposed method outperforms the state-of-the-art feature selection methods by approximately 3.9% when extracting the most important feature.

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Section: Classifiersmentioning

confidence: 99%

Semi-Supervised Feature Selection of Educational Data Mining for Student Performance Analysis

Yu,

Cai,

Pan

et al. 2024

Electronics

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“…Random Forest generates K numbers of trees with various attributes each time, without trimming, by selecting the attributes at random. In contrast to Random Forest, where the test data are evaluated on every produced tree, the most frequent output is then allocated to that instance, in Decision Tree, the test data is tested on just one constructed tree [16]. In general, a random forest with more trees will be more robust.…”

Section: Random Forest Classifiermentioning

confidence: 99%

“…A driving classification model based on a few selected parameters was framed. The classification was also done manually, and then the driving classification model was fitted into SVM, AdaBoost, and Random Forest [1,2,8,16]. The proposed technique was tested for the accuracy of behavior classification.…”

Section: Introductionmentioning

confidence: 99%

Driving behavior analysis and classification by vehicle OBD data using machine learning

Kumar

Jain

2023

J Supercomput

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The transportation industry’s focus on improving performance and reducing costs has driven the integration of IoT and machine learning technologies. The correlation between driving style and behavior with fuel consumption and emissions has highlighted the need to classify different driver’s driving patterns. In response, vehicles now come equipped with sensors that gather a wide range of operational data. The proposed technique collects critical vehicle performance data, including speed, motor RPM, paddle position, determined motor load, and over 50 other parameters through the OBD interface. The OBD-II diagnostics protocol, the primary diagnostic process used by technicians, can acquire this information via the car’s communication port. OBD-II protocol is used to acquire real-time data linked to the vehicle’s operation. This data are used to collect engine operation-related characteristics and assist with fault detection. The proposed method uses machine learning techniques, such as SVM, AdaBoost, and Random Forest, to classify driver’s behavior based on ten categories that include fuel consumption, steering stability, velocity stability, and braking patterns. The solution offers an effective means to study driving behavior and recommend corrective actions for efficient and safe driving. The proposed model offers a classification of ten driver classes based on fuel consumption, steering stability, velocity stability, and braking patterns. This research work uses data extracted from the engine’s internal sensors via the OBD-II protocol, eliminating the need for additional sensors. The collected data are used to build a model that classifies driver’s behavior and can be used to provide feedback to improve driving habits. Key driving events, such as high-speed braking, rapid acceleration, deceleration, and turning, are used to characterize individual drivers. Visualization techniques, such as line plots and correlation matrices, are used to compare drivers’ performance. Time-series values of the sensor data are considered in the model. The supervised learning methods are employed to compare all driver classes. SVM, AdaBoost, and Random Forest algorithms are implemented with 99%, 99%, and 100% accuracy, respectively. The suggested model offers a practical approach to examining driving behavior and suggesting necessary measures to enhance driving safety and efficiency.

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“…Students' course performance depends on many elements, including their own learning characteristics, their own professional orientation, the degree of study in the course, the degree of review after class, the teacher of the class, and other factors. is course will incorporate the improved random forest algorithm into the student course prediction data through the IRFC to enhance the predicted results for the students' achievement in each course; based on this, managers can pay attention to the factors that affect student performance and pay attention to these factors in the course teaching process [25,26]. By continuously cycling this predict-practice-improve approach, we can enhance the teaching and learning reform efforts to make it more scientific and efficient and at the same time make students' performance better and enhance their competitiveness in job opportunities after graduation.…”

Section: Data Introductionmentioning

confidence: 99%

Student Performance Prediction in Mathematics Course Based on the Random Forest and Simulated Annealing

Huang¹,

Wei

2022

Scientific Programming

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Educational data mining is becoming a more and more popular research field in recent years, mainly with the help of cross research conducted by various disciplines, so as to solve various difficult problems in the teaching and education process. In this paper, we proposed a hybrid approach for student performance prediction. We collected the dataset, including 15 characteristics of students from three categories (individual basic information, individual education information, and individual behavior information). Based on the random forest (RF) and simulated annealing (SA) algorithms, we binary encode the relevant parameters (number of features, tree size, and tree decision weights) as the target variables for algorithm optimization, use the out-of-bag error as the optimization objective function, and then propose the IRFC (improved random forest classifier) algorithm in this paper. Compared with other mainstream improved random forest algorithms, the research results demonstrate that the proposed algorithm in this paper has higher generalization ability and smaller OOB error. This study provides a methodological reference for the prediction of student achievement and also makes a marginal contribution to student management work.

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Clarify of the Random Forest Algorithm in an Educational Field

Cited by 24 publications

References 7 publications

Semi-Supervised Feature Selection of Educational Data Mining for Student Performance Analysis

Semi-Supervised Feature Selection of Educational Data Mining for Student Performance Analysis

Driving behavior analysis and classification by vehicle OBD data using machine learning

Student Performance Prediction in Mathematics Course Based on the Random Forest and Simulated Annealing

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