Öznitelik Seçim Yöntemlerinin Toplam Ekipman Etkinliği Tahmin Başarısı Üzerindeki Etkisinin Araştırılması

Abstract

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.

An evaluation of variable selection methods using Southern Africa solar irradiation data

“…Models were fitted on the training data frames and then variable selection capabilities were analysed using the testing data frames. The partitioning strategy avoids the overfitting problem, improves prediction based on bias and/or variance, assesses how effectively the model will perform in real-world scenarios and allows for predicting how well a model will perform on data that it has not seen before (Yilmaz and Kuvat, 2023). The embedded methods were also classified into two groups, namely the shrinkage methods (lasso, elastic net, ridge, PQR) and tree-based methods (RF, RRF, QRF).…”