A Comparative Analysis of the Performance of Three Machine Learning Algorithms for Tweets on Nigerian dataset

Tabra, Maimuna Salisu

doi:10.17781/p002314

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Various data mining methods have been used by different researchers for estimating preferable algorithms (Mythili and Mohamed, 2014;Nikolovski et al,, 2015). In general Data mining processes follows a set of steps that must be executed regardless of the algorithms or methodology that will be implemented (Tabra and Lawan, 2017).…”

Section: Methodsmentioning

confidence: 99%

Application of classification models to predict students’ academic performance using classifiers ensemble and synthetic minority over sampling techniques

Y¹,

Abdulwahab²

2019

Bayero J. Pure App. Sci.

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The demand for data-driven decision making has resulted in the application of data mining in the educational sector and other disciplines. The needs for improving the performance of data mining models have been identified as an interesting area of research institutions keep a large amount of students' data, but these data are rarely used effectively in decision and or policy-making processes. This research is an attempt to enhance the performance of data mining models to predict ensemble and synthetic minority over IBK and SMO were trained and tested on 206 students' data set using previous academic performance records of Federal University Dutse, Nigeria. WEKA 3.9.1 data mining tool was used in predicting the final year student's classes of degree at an undergraduate level, while Unified Tertiary Matriculation Examination, Senior Secondary Certificate Examinations Cumulative Grade Point Average of students served as inputs to the model. The result obtained showed that on training dataset after class balancing, performing the other three (3) classifiers models in RSME (0.1098), suggesting that stacking classifiers ensemble is the best model in context of this research.

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Section: Methodsmentioning

confidence: 99%

Application of classification models to predict students’ academic performance using classifiers ensemble and synthetic minority over sampling techniques

Y¹,

Abdulwahab²

2019

Bayero J. Pure App. Sci.

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“…A cross validation test result [5] indicates that data mining techniques predicted significantly better than its statistical counterpart. Therefore, [6] suggested that with the increasing need for data mining and analyses, there is a need for improving the performance of data mining models and machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

Classifiers ensemble and synthetic minority oversampling techniques for academic performance prediction

Yusuf¹,

John²

2019

IJ-ICT

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The increasing need for data driven decision making recently has resulted in the application of data mining in various fields including the educational sector which is referred to as educational data mining. The need for improving the performance of data mining models has also been identified as a gap for future researcher. In Nigeria, higher educational institutions collect various students’ data, but these data are rarely used in any decision or policy making to improve the academic performance of students. This research work, attempts to improve the performance of data mining models for predicting students’ academic performance using stacking classifiers ensemble and synthetic minority over-sampling techniques. The research was conducted by adopting and evaluating the performance of J48, IBK and SMO classifiers. The individual classifiers models, standard stacking classifier ensemble model and stacking classifiers ensemble model were trained and tested on 206 students’ data set from the faculty of science federal university Dutse. Students’ specific previous academic performance records at Unified Tertiary Matriculation Examination, Senior Secondary Certificate Examination and first year Cumulative Grade Point Average of students are used as data inputs in WEKA 3.9.1 data mining tool to predict students’ graduation classes of degrees at undergraduate level. The result shows that application of synthetic minority over-sampling technique for class balancing improves all the various models performance with the proposed modified stacking classifiers ensemble model outperforming the various classifiers models in both performance accuracy and RSME values making it the best model.<strong></strong>

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Sentiment Analysis of Application User Feedback in Bahasa Indonesia Using Multinomial Naive Bayes

Wiratama

Rusli

2019

2019 5th International Conference on New Media Studies (CONMEDIA)

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A Comparative Analysis of the Performance of Three Machine Learning Algorithms for Tweets on Nigerian dataset

Cited by 3 publications

References 7 publications

Application of classification models to predict students’ academic performance using classifiers ensemble and synthetic minority over sampling techniques

Application of classification models to predict students’ academic performance using classifiers ensemble and synthetic minority over sampling techniques

Classifiers ensemble and synthetic minority oversampling techniques for academic performance prediction

Sentiment Analysis of Application User Feedback in Bahasa Indonesia Using Multinomial Naive Bayes

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