Visualizing the Yield Pattern for Multi Class Classification

This research attempts to generate an automatic prediction model in a hard disk media manufacturing process. This is to be done without human visual interpretation. Our research demonstrates that it can be achieved by visualizing the historical temporal data pattern generated from the inspection machine. From there, the data pattern is transformed and mapped into machine learning algorithm for training. In this paper, we have introduced the pattern visualization technique with trinary and quinary number and compared them with our previous binary pattern visualization technique. This is to deal with multi class classification. The result implied that, the performance of the multi class classification can be improved when all class instances were made higher in quantity and balance. Quinary pattern visualization techniques performed better compared with binary and trinary patterns when the multi class instances were made balanced and were significantly at higher quantity.