A unified view of feature selection based on Hilbert-Schmidt independence criterion

Wang, Tinghua; Hu, Zhiheng; Liu, Harry

doi:10.1016/j.chemolab.2023.104807

Cited by 6 publications

(1 citation statement)

References 45 publications

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“…In short, the high dimensionality leads to challenges of high computational cost, difficulties in model interpretation, and hampering prediction models' generalization abilities. For this reason, feature selection is required before the model is trained (Wang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

YILMAZ,

KUVAT

2023

UUJFE

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Overall equipment effectiveness (OEE) describes production efficiency by combining availability, performance, and quality and is used to evaluate production equipment’s performance. This research’s aim is to investigate the potential of the feature selection techniques and the multiple linear regression method, which is one of the machine learning techniques, in successfully predicting the OEE of the corrugated department of a box factory. In the study, six different planned downtimes and information on seventeen different previously known concepts related to activities to be performed are used as input features. Moreover, backward elimination, forward selection, stepwise selection, correlation-based feature selection (CFS), genetic algorithm, random forest, extra trees, ridge regression, lasso regression, and elastic net feature selection methods are proposed to find the most distinctive feature subset in the dataset. As a result of the analyses performed on the data set consisting of 23 features, 1 output and 1204 working days of information, the elastic net - multiple linear regression model, which selects 19 attributes, gave the best average R2 value compared to other models developed. Occam's razor principle is taken into account since there is not a great difference between the average R2 values obtained. Among the models developed according to the principle, the stepwise selection - multiple linear regression model yielded the best R2 value among those that selected the fewest features.

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Section: Introductionmentioning

confidence: 99%