Proceedings ArticleDOI
Comparisons of classifier algorithms: Bayesian network, C4.5, decision forest and NBTree for Course Registration Planning model of undergraduate students
P. Pumpuang,Anongnart Srivihok,P. Praneetpolgrang +2 more
- pp 3647-3651
TLDR
NBTree was used to generate CRP model which can be used to predict student class of GPA and consider student course sequences for registration planning and showed that NBTree seemed to be the best of four classifiers which had highest prediction power.Abstract:
The success rate of computer science and engineering students in private universities are not high. It is helpful to find the model to assist students in registration planning. The objective of this research is to propose the classifier algorithm for building course registration planning model (CRPM) from historical dataset. The algorithm is selected by comparing performances of four classifiers include Bayesian network, C4.5, Decision Forest and NBTree. The dataset were obtained from student enrollments including grade point average (GPA) and grades of undergraduate students whose majors were computer science or computer engineering. These dataset included grades in each subject of first and second year students from a private university in Thailand. Results showed that NBTree seemed to be the best of four classifiers which had highest prediction power. NBTree was used to generate CRP model which can be used to predict student class of GPA and consider student course sequences for registration planning.read more
Citations
More filters
Posted Content
Performance Analysis and Prediction in Educational Data Mining: A Research Travelogue
TL;DR: A comprehensive survey, a travelogue (2002-2014) towards educational data mining and its scope in future.
Journal ArticleDOI
Correlation based feature selection (cfs) technique to predict student perfromance
Mital Doshi,Setu K Chaturvedi +1 more
TL;DR: This paper has tried to implement a classification technique to assist students in predicting their success in admission in an engineering stream using feature selection attribute algorithms Chi-square, GainRatio, andInfoGain to predict the relevant features.
Journal ArticleDOI
An Empirical Evaluation of Machine Learning Techniques for Chronic Kidney Disease Prophecy
TL;DR: Experiential analysis of ML techniques for classifying the kidney patient dataset as CKD or NOTCKD shows that CHIRP performs well in terms of diminishing error rates and improving accuracy.
Journal ArticleDOI
Using machine learning and big data for efficient forecasting of hotel booking cancellations
TL;DR: A means of enabling the forecasting of hotel booking cancellations using only 13 independent variables, a reduced number in comparison with related research in the area, which in addition coincide with those that are most often requested by customers when they place a reservation.
Journal Article
Performance Analysis and Prediction in Educational Data Mining: A Research Travelogue
TL;DR: A comprehensive survey of educational data mining can be found in this article, where the authors present a travelogue of the field of education data mining and its scope in the future, as well as a review of the current state of the art.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Book
Data Mining: Practical Machine Learning Tools and Techniques
TL;DR: This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining.
Journal ArticleDOI
Induction of Decision Trees
TL;DR: In this paper, an approach to synthesizing decision trees that has been used in a variety of systems, and it describes one such system, ID3, in detail, is described, and a reported shortcoming of the basic algorithm is discussed.
Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
Book
Data Mining
TL;DR: In this paper, generalized estimating equations (GEE) with computing using PROC GENMOD in SAS and multilevel analysis of clustered binary data using generalized linear mixed-effects models with PROC LOGISTIC are discussed.