Optimizing random forest classifier with Jenesis-index on an imbalanced dataset

Zeffora, Joylin; Shobarani, Shobarani

doi:10.11591/ijeecs.v26.i1.pp505-511

Cited by 4 publications

(3 citation statements)

References 22 publications

(22 reference statements)

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“…Random Forest builds many decision trees using random subsets of the training data and characteristics. Individual predictions are made by each tree, and then the group predictions are combined by voting or averaging [18], [21], [22].…”

Section: Use Of Random-forest Algorithms For Prediction Of Math Scorementioning

confidence: 99%

Evaluating various machine learning methods for predicting students' math performance in the 2019 TIMSS

Elouafi,

Tammouch,

Eddarouich

et al. 2024

IJEECS

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The growth of a country strongly depends on the quality of its educational system. All over the world, the education sectors are experiencing a fundamental evolution of their mode of operation. The greatest challenge for education today is the low success rate of learners and the abandonment of education in institutions at a premature age. Early prediction of student failure can help administrators provide timely guidance and supervision to enhance student success and retention. We propose a performance prediction model based on students' social and academic integration using several classification algorithms. This study involves a comparative analysis of five algorithms: logistics regression, k-nearest neighbors (K-NN), support vector machine (SVM), decision tree, and random forest. They were applied to a set of data from TIMSS 2019 in Morocco, to determine their effectiveness in predicting student performance using prediction models such as logistics regression, KNN, SVM, decision-tree, and random forest, decision-makers can make data-driven decisions to enhance educational strategies and improve outcomes in mathematics education.

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Section: Use Of Random-forest Algorithms For Prediction Of Math Scorementioning

confidence: 99%

Evaluating various machine learning methods for predicting students' math performance in the 2019 TIMSS

Elouafi,

Tammouch,

Eddarouich

et al. 2024

IJEECS

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“…Random forest classification selects a random subset from training data [30]. This algorithm is used to generate accurate predictions [31]. Ì 231…”

Section: Random Forestmentioning

confidence: 99%

The Effect of Class Imbalance Handling on Datasets Toward Classification Algorithm Performance

Kaope,

Pristyanto

2023

matrik

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Class imbalance is a condition where the amount of data in the minority class is smaller than that of the majority class. The impact of the class imbalance in the dataset is the occurrence of minority class misclassification, so it can affect classification performance. Various approaches have been taken to deal with the problem of class imbalances such as the data level approach, algorithmic level approach, and cost-sensitive learning. At the data level, one of the methods used is to apply the sampling method. In this study, the ADASYN, SMOTE, and SMOTE-ENN sampling methods were used to deal with the problem of class imbalance combined with the AdaBoost, K-Nearest Neighbor, and Random Forest classification algorithms. The purpose of this study was to determine the effect of handling class imbalances on the dataset on classification performance. The tests were carried out on five datasets and based on the results of the classification the integration of the ADASYN and Random Forest methods gave better results compared to other model schemes. The criteria used to evaluate include accuracy, precision, true positive rate, true negative rate, and g-mean score. The results of the classification of the integration of the ADASYN and Random Forest methods gave 5% to 10% better than other models.

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“…The results demonstrate the potential for understanding basic emotional feelings in individuals. Zeffora and Shobarani [9] proposed an adaptive attribute selection method for the random forest, considering structural changes in datasets. Applied to myocardial infarction data, it improves accuracy and avoids under/over-fitting.…”

Section: Introductionmentioning

confidence: 99%

Enhancing data retrieval efficiency in large-scale JavaScript object notation datasets by using indexing techniques

Srisungsittisunti,

Duangkaew,

Mekruksavanich

et al. 2024

IJ-AI

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<div class="translate-tooltip-mtz green sm-root translate hidden_translate"><div class="header-wrapper"><div class="header-controls"><span>The use of JavaScript Object Notation (JSON) format as a Not only Structured Query Language (NoSQL) storage solution has grown in popularity, but has presented technical challenges, particularly in indexing large-scale JSON files. This has resulted in slow data retrieval, especially for larger datasets. In this study, we propose the use of JSON datasets to preserve data in resource survey processes. We conducted experiments on a 32-gigabyte dataset containing 1,000,000 transactions in JSON format and implemented two indexing methods, dense and sparse, to improve retrieval efficiency. Additionally, we determined the optimal range of segment sizes for the indexing methods. Our findings revealed that adopting dense indexing reduced data retrieval time from 15,635 milliseconds to 55 milliseconds in one-to-one data retrieval, and from 38,300 milliseconds to 1 millisecond in the absence of keywords. In contrast, using sparse indexing reduced data retrieval time from 33,726 milliseconds to 36 milliseconds in one-to-many data retrieval and from 47,203 milliseconds to 0.17 milliseconds when keywords were not found. Furthermore, we discovered that the optimal segment size range was between 20,000 and 200,000 transactions for both dense and sparse indexing.</span></div></div></div>

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Optimizing random forest classifier with Jenesis-index on an imbalanced dataset

Cited by 4 publications

References 22 publications

Evaluating various machine learning methods for predicting students' math performance in the 2019 TIMSS

Evaluating various machine learning methods for predicting students' math performance in the 2019 TIMSS

The Effect of Class Imbalance Handling on Datasets Toward Classification Algorithm Performance

Enhancing data retrieval efficiency in large-scale JavaScript object notation datasets by using indexing techniques

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