Forecasting of Campus Placement for Students Using Ensemble Voting Classifier

Dutta, Shawni; Bandyopadhyay, Samir Kumar

doi:10.9734/ajrcos/2020/v5i430138

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…They also proposed novel hybrid classifiers to gain accurate predictions of student performance. The results showed that the hybrid model outperformed the other classifiers in terms of accuracy (i.e., 81.67) precision (i.e., 79.62), recall (i.e., 75.86), and F-score (i.e., 77.69) in comparison to base classifiers and ensemble techniques applied in the same research [54].…”

Section: Comparison Of Applied Approach With Existing Approachesmentioning

confidence: 95%

“…The majority voting reflects more than 50% votes for the final decision; in unanimous voting, all classifiers develop an agreement for final decisions, whereas polarity voting considers the majority of votes to decide the final outcome. In this research, majority voting was used to combine classifiers because it provides better results in terms of accuracy, as indicated by prior research [53,54]. Moreover, three different algorithms including Naive Bayes, IBk, and ZeroR were used in this study for voting.…”

Section: Votingmentioning

confidence: 99%

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Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

et al. 2021

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In the past few years, educational data mining (EDM) has attracted the attention of researchers to enhance the quality of education. Predicting student academic performance is crucial to improving the value of education. Some research studies have been conducted which mainly focused on prediction of students’ performance at higher education. However, research related to performance prediction at the secondary level is scarce, whereas the secondary level tends to be a benchmark to describe students’ learning progress at further educational levels. Students’ failure or poor grades at lower secondary negatively impact them at the higher secondary level. Therefore, early prediction of performance is vital to keep students on a progressive track. This research intended to determine the critical factors that affect the performance of students at the secondary level and to build an efficient classification model through the fusion of single and ensemble-based classifiers for the prediction of academic performance. Firstly, three single classifiers including a Multilayer Perceptron (MLP), J48, and PART were observed along with three well-established ensemble algorithms encompassing Bagging (BAG), MultiBoost (MB), and Voting (VT) independently. To further enhance the performance of the abovementioned classifiers, nine other models were developed by the fusion of single and ensemble-based classifiers. The evaluation results showed that MultiBoost with MLP outperformed the others by achieving 98.7% accuracy, 98.6% precision, recall, and F-score. The study implies that the proposed model could be useful in identifying the academic performance of secondary level students at an early stage to improve the learning outcomes.

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Section: Comparison Of Applied Approach With Existing Approachesmentioning

confidence: 95%

Section: Votingmentioning

confidence: 99%

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

et al. 2021

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“…Employability is the need vital no matter what the course picked by a student in high level training. The weakness of the consequence of guidance can be given a silver lining through the employability estimate model [14] [15]. Preparing today is fundamental for a merciless overall market that keeps on changing rapidly in light of mechanical advances.…”

Section: Literature Reviewmentioning

confidence: 99%

Developing intelligent hybrid DNN model for predicting students’ employability – A Machine Learning approach

Sun,

2023

EHSS

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The employability of students is an important concern for colleges nowadays, and forecasting it can assist institutions take prompt action to improve the institutional placement ratio. The most effective data mining methods for forecasting students' employability are categorization methods. Students can concentrate on areas where they need to improve to better match the company's skill set by being aware of their deficiencies prior to an interview. Also, forecasting student employability might assist academic staff in developing curricular plans. In order for kids, parents, guardians, organisations, and businesses to profit to some extent, this report offered a fresh, futuristic roadmap. Out of seven trials, the first five were carried out using in-depth statistical calculations, while the latter two were carried out using supervised machine learning techniques. On just one extreme, the Support Vector Machine (SVM) had the highest accuracy rate of 93% when it related to forecasting employment status. The Random Forest (RF), on the other hand, was able to identify the gender of placed kids with a maximum accuracy of 89%. It is also advised to determine the placement of gender and placement status using a number of key criteria. A statistical t-test with a significance threshold of 0.05 demonstrated that the student’s gender had no impact on the pay that was provided during job placement.

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Improving classification algorithm on education dataset using hyperparameter tuning

Muhajir

Akbar

Bagaskara

et al. 2022

Procedia Computer Science

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Forecasting of Campus Placement for Students Using Ensemble Voting Classifier

Cited by 8 publications

References 16 publications

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

Predicting Academic Performance Using an Efficient Model Based on Fusion of Classifiers

Developing intelligent hybrid DNN model for predicting students’ employability – A Machine Learning approach

Improving classification algorithm on education dataset using hyperparameter tuning

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