ClassificationClassification which is used to predict the classes of objects has the disadvantage of ignoring the costsCost incurred in false predictionsPrediction. However, wrong predictionsPrediction can cause different degrees of costsCost. Therefore, cost-sensitiveCost-sensitiveclassificationClassification algorithms are in demand in order to improve qualityQuality of the classification. In this study, rule-based cost sensitiveCost-sensitiveBEE-minerBEE-miner algorithm which was developed by making use of Bees AlgorithmBees Algorithmand MEPAR-minerMEPAR-miner algorithms are used to classify the defectsDefects in the production line of a textileTextile,Production,Rule-based company in a considerably better way. When results on the qualityQuality defect dataset are analysed, it is observed that BEE-minerBEE-miner algorithm outperforms the MEPAR-minerMEPAR-miner algorithm in terms of classificationClassificationcostCost and accuracy.


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    Title :

    A Case Study with the BEE-Miner Algorithm: Defects on the Production Line


    Additional title:

    Springer Ser.Advanced Manufacturing


    Contributors:


    Publication date :

    2022-11-20


    Size :

    15 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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