CLASSIFYING DEFECTIVE AND NON-DEFECTIVE PRODUCTS USING LDA AND VOTING CLASSIFIER IN QUALITY CONTROL PROCESSES

Abstract

This research explores the application of machine learning techniques in classifying defective and non-defective products within a quality control process. Two models, Linear Discriminant Analysis (LDA) and a Voting Classifier, were evaluated for their performance in identifying defective items. The study utilized a wine quality dataset, where the 'quality' attribute was binarized into defective and non-defective classes. The models were assessed based on their classification accuracy, precision, recall, and other evaluation metrics. The LDA model achieved a test set accuracy of 72.71%, with balanced precision and recall values for both classes. It demonstrated a precision of 0.68 and a recall of 0.74 for the non-defective class (Class 0) and a precision of 0.78 and a recall of 0.72 for the defective class (Class 1). These results highlight the model’s ability to handle the classification task with reasonable accuracy and consistency. In comparison, the Voting Classifier significantly outperformed LDA on the test set, achieving an accuracy of 81.04%. It showed a higher precision (0.79) and recall (0.77) for the non-defective class and an impressive precision (0.82) and recall (0.84) for the defective class. These results underline the robustness of the Voting Classifier in handling complex classification tasks with improved reliability and performance. The findings indicate that while LDA provides baseline performance, the Voting Classifier demonstrates superior capabilities in defect detection, making it a better candidate for quality control applications. This study emphasizes the importance of model selection in optimizing testing outcomes for industrial processes.

Keywords Key words: Defective Classification, Machine Learning, Linear Discriminant Analysis (LDA), Voting Classifier.