Academic performance prediction based on voting technique

Azmi, Muhammad Sufyian Bin Mohd; Paris, Ikmal Hisyam Bin Mohamad

doi:10.1109/iccsn.2011.6014841

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…Voting or vote classifier used to ensembles of more than one classifier. The approach is based on plurality or majority voting, where each single classifier contributes a single vote [20]. The aggregation prediction is decided by the majority of the votes, i.e., the class with the most votes is the final prediction.…”

Section: Introductionmentioning

confidence: 99%

Accelerator-Based Human Activity Recognition Using Voting Technique with NBTree and MLP Classifiers

Azmi¹,

Sulaiman²

2017

International Journal on Advanced Science, Engineering and Information Technology

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In evolution and ubiquitous computing systems, accelerometer-based human activity recognition has huge potential in a large number of application domains. Accelerometer-based human activity recognition aims to identify physical activities performed by human using accelerometer; a sensor device attached to the body and returns an actual valued estimate of acceleration along the x, y-and z-axes from which the sensor location can be estimated. In this study, an accelerator-based activity recognition model using voting technique was proposed. Two machine learning classifiers, Naïve Bayes Tree (NBTree) and Multilayer Perceptron (MLP), were used as ensemble classifiers in the voting technique. To evaluate the proposed voting technique, the performance of selected individual classifiers and existing voting technique was first examined, followed by the experiment to determine the performance of the proposed model. All of the experiments were performed using a standard dataset called Wireless Sensor Data Mining involving six physical human activities; jogging, walking, walking towards upstairs, walking towards downstairs, sitting and stand still. Results showed that the proposed voting technique with NBTree and MLP ensemble classifiers outperformed other individual classifiers and another previously suggested voting technique for accelerometer-based human activity recognition.

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Section: Introductionmentioning

confidence: 99%

Accelerator-Based Human Activity Recognition Using Voting Technique with NBTree and MLP Classifiers

Azmi¹,

Sulaiman²

2017

International Journal on Advanced Science, Engineering and Information Technology

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“…Another work carried out was that of Nghe et al [4], used decision trees and Bayesian Network algorithms to predict students' performance in the 3rd year based on the data obtained in the 2nd year. Thus, like M. Azmi and I. Paris [5] with a work based on the same method. In another work, SB Kotsiantis [6], compared the prediction of six different methods, namely decision trees (DTs), support vector machines (SVM), naive bayes, artificial neural network (ANR), and with the K-NN algorithm to predict student dropout in the middle of the course.…”

Section: Literature Reviewmentioning

confidence: 93%

Students Drop Out Trends: A University Study

Silva

Pires

Reis

et al. 2021

Communications in Computer and Information Science

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The dropout of university students has been a factor of concern for educational institutions, affecting various aspects such as the institution's reputation and funding and rankings. For this reason, it is essential to identify which students are at risk . In this study, algorithms based on decision trees and random forests are proposed to solve these problems using real data from 331 students from the University of Trásos-Montes and Alto Douro. In this work with these learning algorithms together with the training strategies , we managed to obtain an 89% forecast of students who may abandon their studies based on the evaluations of both semesters related to the first year and personal data.

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“…CART does not rely on distribution of data. Outliers have less impact on CART [39]. The tuning parameter used is feature selection method which is either information gain or gini index.…”

Section: Cartmentioning

confidence: 99%

Comparative Study of Supervised Algorithms for Prediction of Students’ Performance

Sathe¹,

Adamuthe²

2021

IJMECS

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Predicting academic performance of the student is crucial task as it depends on various factors. To perform such predictions the machine learning and data mining algorithms are useful. This paper presents investigation of application of C5.0, J48, CART, Naï ve Bayes (NB), K-Nearest Neighbour (KNN), Random Forest and Support Vector Machine for prediction of students' performance. Three datasets from school level, college level and e-learning platform with varying input parameters are considered for comparison between C5.0, NB, J48, Multilayer Perceptron (MLP), PART, Random Forest, BayesNet, and Artificial Neural Network (ANN). Paper presents comparative results of C5.0, J48, CART, NB, KNN, Random forest and SVM on changing tuning parameters. The performance of these techniques is tested on three different datasets. Results show that the performances of Random forest and C5.0 are better than J48, CART, NB, KNN, and SVM.

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Academic performance prediction based on voting technique

Cited by 10 publications

References 5 publications

Accelerator-Based Human Activity Recognition Using Voting Technique with NBTree and MLP Classifiers

Accelerator-Based Human Activity Recognition Using Voting Technique with NBTree and MLP Classifiers

Students Drop Out Trends: A University Study

Comparative Study of Supervised Algorithms for Prediction of Students’ Performance

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