Attribute reduction with personalized information granularity of nearest mutual neighbors

Ju, Hengrong; Ding, Weiping; Shi, Zhongchao; Huang, Jiashuang; Jie, Yang; Yang, Xibei

doi:10.1016/j.ins.2022.09.006

Cited by 16 publications

(5 citation statements)

References 45 publications

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“…Therefore, the proposed method performed better with approximately 5% improvement compared to the best method in Table 8. The best improvement (23.86%) achieved over the RBF-SVM method [22]. Accordingly, the LMT forest model can be successfully utilized in steel product manufacturing with the objective of fault prediction and thus making the necessary arrangements to handle faults with regard to the high accuracy of our presented model.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 87%

“…In this section, the proposed LMT forest method is compared with the state-of-theart methods [20][21][22][23][24][25][26][27][28][29][30][31][32]. The results of previous studies on the same dataset are given in Table 8.…”

Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 99%

“…(iv) In the experiments, the proposed LMT forest method with an accuracy of 86.655% outperformed the random forest method with an accuracy of 79.547% on the same dataset. (v) Our method achieved higher classification accuracy than the state-of-the-art methods [20][21][22][23][24][25][26][27][28][29][30][31][32] on the same steel plate fault dataset and demonstrated its superiority over its counterparts.…”

Section: Introductionmentioning

confidence: 93%

“…It covers attribute properties of integer and real values. Since 2010, this dataset has been widely utilized in the literature for contributing to the analysis of various methods over steel plate faults [20][21][22][23][24][25][26][27][28][29][30][31][32]. The dataset comprises 1941 records with different fault-type labels that can occur on steel surfaces.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…The proposed model (86.65%) achieved higher performance when compared to the state-of-the-art methods [20][21][22][23][24][25][26][27][28][29][30][31][32] in terms of accuracy. Improvements ranging from 5 to 24% were demonstrated compared to the aforementioned methods.…”

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confidence: 99%

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Logistic Model Tree Forest for Steel Plates Faults Prediction

et al. 2023

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Fault prediction is a vital task to decrease the costs of equipment maintenance and repair, as well as to improve the quality level of products and production efficiency. Steel plates fault prediction is a significant materials science problem that contributes to avoiding the progress of abnormal events. The goal of this study is to precisely classify the surface defects in stainless steel plates during industrial production. In this paper, a new machine learning approach, entitled logistic model tree (LMT) forest, is proposed since the ensemble of classifiers generally perform better than a single classifier. The proposed method uses the edited nearest neighbor (ENN) technique since the target class distribution in fault prediction problems reveals an imbalanced dataset and the dataset may contain noise. In the experiment that was conducted on a real-world dataset, the LMT forest method demonstrated its superiority over the random forest method in terms of accuracy. Additionally, the presented method achieved higher accuracy (86.655%) than the state-of-the-art methods on the same dataset.

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Section: Comparison With the State-of-the-art Methodsmentioning

confidence: 87%