Defining the Ranges Boundaries of the Optimal Parameters Values for the Random Forest Classifier

Demidova, Liliya; Ivkina, Mariya

doi:10.1109/summa48161.2019.8947569

Cited by 11 publications

(3 citation statements)

References 8 publications

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“…In the modeling stage using the Random Forest algorithm, adjustments were made to several key parameters to enhance model performance (Demidova & Ivkina, 2019;Kurniawati, Novita Nurmala Putri, & Kurnia Ningsih, 2020). The number of decision trees used is 100, and the assigned random state value is 42.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

Wholesale Inventory Management Optimization: Methodological Approach with XGBoost, SVR, and Random Forest Algorithms

Hutagalung,

Rosalind,

Tuhu

et al. 2023

Brilliance

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This research aims to optimize wholesale inventory management at PT Primafood International Pasir Putih 2 by implementing leading algorithms, namely XGBoost, Support Vector Regression (SVR), and Random Forest. In the wholesale industry, effective inventory management plays a crucial role in maintaining smooth production processes and enhancing company profitability. Despite the acknowledged benefits of inventory management, there are aspects that remain not fully disclosed, particularly concerning demand uncertainty and market fluctuations. This study addresses these gaps by exploring the potential of these three algorithms. Experimental methods with a quantitative approach were employed to shape and prepare the dataset. The analysis and predictions' results using XGBoost, SVR, and Random Forest were evaluated using metrics such as Mean Squared Error (MSE), F1-Score, and Accuracy. The evaluation indicates that XGBoost and SVR exhibit optimal performance with low MSE values of 7714.446 and 119.315, high F1-Scores (0.92), and good accuracy levels (0.86 and 0.85), respectively. While Random Forest shows a higher MSE, it still delivers solid performance with an F1-Score of 0.89 and an accuracy rate of 0.81. These findings suggest that all three algorithms can be considered to enhance inventory management performance at PT Primafood International Pasir Putih 2, with significant potential benefits for overall industry development. This research provides valuable insights for decision-making at the business and industrial levels, highlighting the effectiveness of each algorithm in the context of predicting stock level.

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Section: Random Forest Algorithmmentioning

confidence: 99%

Wholesale Inventory Management Optimization: Methodological Approach with XGBoost, SVR, and Random Forest Algorithms

Hutagalung,

Rosalind,

Tuhu

et al. 2023

Brilliance

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show abstract

“…When the classification tests are reached, the classification's final result is determined by a ballot on the DT. Scholars usually start by raising the accuracy of the classifier and then decreasing the interaction among classification models [54]. The final reduction of the classification effect is achieved using the RF method in the classifier, where the outcomes of each base classifier's classification have a similar error distribution.…”

Section: B Random Forests Classifiermentioning

confidence: 99%

Machine Learning Classifiers Based Classification For IRIS Recognition

Chicho

Abdulazeez

Zeebaree

et al. 2021

QAJ

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Classification is the most widely applied machine learning problem today, with implementations in face recognition, flower classification, clustering, and other fields. The goal of this paper is to organize and identify a set of data objects. The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. The results of the comparison analysis showed that the K-nearest neighbors outperformed the other classifiers. Also, the random forest classifier worked better than the decision tree (j48). Finally, the best result obtained by this study is 100% and there is no error rate for the classifier that was obtained.

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“…Examples of classifier methods are [14] Decision Tree (DT), Support Vector Machine (SVM), Artificial Neural Network (ANN), k-Nearest Neighbor (kNN), etc. The classifier needs the correct choice of parameters to produce high quality classification [15]. For example, the SVM classifier uses the kernel function parameter to solve nonlinear multi classification problems [16].…”

Section: Feature Extractor and Classifiermentioning

confidence: 99%

The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition

2021

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Security systems must be continuously developed in order to cope with new challenges. One example of such challenges is the proliferation of sexual harassment against women in public places, such as public toilets and public transportation. Although separately designated toilets or waiting and seating areas in public transports are provided, enforcing these restrictions need constant manual surveillance. In this paper we propose an automatic gender classification system based on an individual’s facial characteristics. We evaluate the performance of QLRBP and MLLPQ as feature extractors combined with SVM or kNN as classifiers. Our experiments show that MLLPQ gives superior performance compared to QLRBP for either classifier. Furthermore, MLLPQ is less computationally demanding compared to QLRBP. The best result we achieved in our experiments was the combination of MLLPQ and kNN classifier, yielding an accuracy rate of 92.11%.

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Defining the Ranges Boundaries of the Optimal Parameters Values for the Random Forest Classifier

Cited by 11 publications

References 8 publications

Wholesale Inventory Management Optimization: Methodological Approach with XGBoost, SVR, and Random Forest Algorithms

Wholesale Inventory Management Optimization: Methodological Approach with XGBoost, SVR, and Random Forest Algorithms

Machine Learning Classifiers Based Classification For IRIS Recognition

The Use of QLRBP and MLLPQ as Feature Extractors Combined with SVM and kNN Classifiers for Gender Recognition

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