ANN Hyperparameter Optimization by Genetic Algorithms for Via Interconnect Classification

In an imbalanced classification problem the distribution of data across the known classes is biased or skewed. It poses a challenge for predictive modeling as most of the machine learning algorithms used for classification were designed around the assumption of an equal number of examples for each class. In this paper, we propose an approach to solve via interconnect classification problems by artificial neural networks, where the optimum hyperparameters of the networks are searched through a genetic algorithm. We solve the binary imbalanced classification problem for vias in time domain and frequency domain, including single and multilabel cases. Imbalanced learning techniques, like random oversampling and weighted binary crossentropy, are studied in combination with the genetic algorithm. We found standardization, F-measure, and imbalanced learning techniques are suitable to deal with minority label classification for this kind of signal integrity problems. The overall accuracy of our method is above 97%.

Subjects

artificial neural networks

genetic algorithm

imbalanced learning

multilabel

via interconnect

MLE@TUHH

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ANN Hyperparameter Optimization by Genetic Algorithms for Via Interconnect Classification