Variable Reduction-based Prediction through Modified Genetic Algorithm

Delima, Allemar Jhone P.; Sison, Ariel M.; Medina, Ruji P.

doi:10.14569/ijacsa.2019.0100544

Cited by 12 publications

(11 citation statements)

References 31 publications

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“…In the study of [27], a hybrid GA-KNN was employed to build a prediction model using records of student responses to reduce the variables in the evaluation instrument of faculty instructional performance in State Colleges and Universities in Caraga Region, Philippines. The study achieved an accuracy of 95.53% and 66.66% data reduction.…”

Section: A Prediction-based Models Applied In Learning Institutionsmentioning

confidence: 99%

A Prediction-based Curriculum Analysis using the Modified Artificial Bee Colony Algorithm

Cutad¹,

Gerardo²

2019

IJACSA

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Due to the vast amount of students' information and the need of quick retrieval, establishing databases is one of the top lists of the IT infrastructure in learning institutions. However, most of these institutions do not utilize them for knowledge discovery which can aid in informed decision-making, investigation of teaching and learning outcomes, and development of prediction models in particular. Prediction models have been utilized in almost all areas and improving the accuracy of the model is sought-after this study. Thus, the study presents a Scoutless Rule-driven binary Artificial Bee Colony (SRABC) as a searching strategy to enhance the accuracy of the prediction model for curriculum analysis. Experimental verification revealed that SRABC paired with K-Nearest Neighbor (KNN) increases the prediction accuracy from 94.14% to 97.59% than paired with Support Vector Machine (SVM) and Logistic Regression (LR). SRABC is efficient in selecting 14 out of 60 variables through majority voting scheme using the data of the BSIT students of Davao Del Norte State College (DNSC),

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Section: A Prediction-based Models Applied In Learning Institutionsmentioning

confidence: 99%

A Prediction-based Curriculum Analysis using the Modified Artificial Bee Colony Algorithm

Cutad¹,

Gerardo²

2019

IJACSA

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“…It also helped in lessening the space and computational time as well as minimizing the size of formulas used by the algorithm on the execution process [13]. Maximized accuracy through the reduced number of attributes [14] and better understandability and interpretability of results are among the many benefits perceived in data reduction [15].…”

Section: Related Literaturementioning

confidence: 99%

Rank-Based Variable Minimization Using Clustering Algorithm

Delima¹,

Francisco²

2019

IJMLC

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This paper introduces variable minimization with the advent of data mining techniques, particularly the cluster analysis using the K-means algorithm. The results of the simulation process validated the actual ranking of the variables in each category from the 407 records of student-respondents. A noticeable decrease of variables in determining the instructional performance of teachers in the Caraga Region, Philippines is evident in the simulation results. The variables that were clustered by the algorithm that conforms to have the lowest rank based on the actual survey was removed. Results showed that there were a total of fourteen (14) variables removed from the thirty-item survey questionnaire. In this case, it only proved that like any other data mining algorithms, the cluster analysis, particularly with the K-Means algorithm, is also effective in determining what variables in the data set to omit based on the groupings with the lowest rank it generated.

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“…Parameters, such as business course, maths course grade, science course grade, and core IT course grade can provide an indication of future students' performance in higher advanced courses. The current approaches have failed to analyse and monitor the progress of the student achievements [5]. Inappropriate methods or investigation procedures can also contribute to failure.…”

Section: Introductionmentioning

confidence: 99%

RHEM: A Robust Hybrid Ensemble Model for Students’ Performance Assessment on Cloud Computing Course

Sakri¹,

Alluhaidan²

2020

IJACSA

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Creating tools, such as a prediction model to assist students in a traditional or virtual setting, is an essential activity in today's educational climate. The early stage towards incorporating these predictive models using techniques of machine learning focused on predicting the achievement of students in terms of the grades obtained. The research aim is to propose a robust hybrid ensemble model (RHEM) that can warn at-risks students (on Cloud Computing course) of their likely outcomes at the early semester assessment. We hybridised four renowned single algorithms-Naïve Bayes, Multilayer Perceptron, k-Nearest Neighbours, and Decision Tablewith four well-established ensemble algorithms-Bagging, RandomSubSpace, MultiClassClassifier, and Rotation Forestwhich produced 16 new hybrid ensemble classifier models. Hence, we have thoroughly and rigorously built, trained, and tested 24 models all together. The experiment concluded that the Rotation Forest + MultiLayer Perceptron model was the best performing model based on the model evaluation in terms of Accuracy (91.70%), Precision (86.1%), F-Score rate (87.3%), and Receiver Operating Characteristics Area detection (98.6%). Our research will help students identify their likely final grades in terms of whether they are excellent, very good, good, pass, or fail, and, thus, transform their academic conduct to achieve higher grades in the final exam accordingly.

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Variable Reduction-based Prediction through Modified Genetic Algorithm

Cited by 12 publications

References 31 publications

A Prediction-based Curriculum Analysis using the Modified Artificial Bee Colony Algorithm

A Prediction-based Curriculum Analysis using the Modified Artificial Bee Colony Algorithm

Rank-Based Variable Minimization Using Clustering Algorithm

RHEM: A Robust Hybrid Ensemble Model for Students’ Performance Assessment on Cloud Computing Course

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