Discretization method to optimize logistic regression on classification of student’s cognitive domain

Yamasari, Yuni; Rusimamto, Puput Wanarti; Rochmawati, Naim; Suyatno, Dwi Fatrianto; Wibawa, Setya Chendra; Nugroho, Sudarmanto Budi; Purnomo, Mauridhi Hery

doi:10.1051/matecconf/201819703006

Cited by 2 publications

(2 citation statements)

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“…Therefore, in this paper, we intend to work on those problems by enhancing the performance of the mining process. For this purpose, we extend the previous work [26] to determine an optimal number of clusters. Then, we propose a hybrid method that combines feature selection methods, namely: a filter-based and a wrapper-based approach, to eliminate irrelevant features and to improve the accuracy level.…”

Section: Descriptionmentioning

confidence: 99%

“…Here, we adopt the discretization method which is called equal-width to discretize the continuous data on student data extracted based on category. Additionally, we do the discretization for three-interval, four-interval, and five-interval as extended to the previous work [26]. For evaluating, we build the logistic regression with two regularizations: lasso, ridge.…”

Section: Identifying the Dominant Characteristicsmentioning

confidence: 99%

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Identifying Dominant Characteristics of Students’ Cognitive Domain on Clustering-based Classification

Yamasari¹,

Nugroho²,

Yoshimoto³

et al. 2020

IJIES

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The rapid growth of information and communications technology-based educational tools generates a large volume of student data with many features (characteristics). However, the mining process in the clustering task of student data is not often done optimally, so the performance of the system decreases. To overcome this problem, we propose a discretization method on logistic regression to determine the most optimal number of clusters. Additionally, we introduce a technique that combines the features selection using a filter-and wrapper-based procedures (HFS) to identify the dominant features of the students' cognitive domains. Furthermore, we evaluate the identification result by three clustering methods, namely: K-means, EM, and Farthest first. Finally, we propose the clustering-based classification so the results can be measured by using the classification metrics. Here, we apply two evaluation techniques, namely: cross-validation and percentage split. The experimental results indicate that our approach describes predominance, in terms of classification metrics over conventional methods. Our approach is around 10,847-11,134 percent higher in terms of accuracy average than the original features on both the assessment techniques. Also, this approach significantly reduces the time taken to create a prototype between 0.0167-0.027 seconds. This gives the impact on a significant reduction in the model created to the number of unsuitable students on classes based on the cognitive domain, namely: 3-12 students.

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Section: Descriptionmentioning

confidence: 99%

Section: Identifying the Dominant Characteristicsmentioning

confidence: 99%