Two-Stage Neural Network Classifier for the Data Imbalance Problem with Application to Hotspot Detection
Journal
Proceedings - Design Automation Conference
Journal Volume
2021-December
Pages
175-180
Date Issued
2021
Author(s)
Abstract
The data imbalance problem often occurs in nanometer VLSI applications, where normal cases far outnumber error ones. Many imbalanced data handling methods have been proposed, such as oversampling minority class samples and downsampling majority class samples. However, existing methods focus on improving the quality of minority classes while causing quality deterioration of majority ones. In this paper, we propose a two-stage classifier to handle the data imbalance problem. We first develop an iterative neural network framework to reduce false alarms. Then the oversampling method on a final classification network is applied to predict the two classes better. As a result, the data imbalance problem is well handled, and the quality deterioration of majority classes is also reduced. Since the iterative stage does not change any existing network structure, any convolutional neural network can be used in the framework. Compared with the state-of-the-art imbalanced data handling methods, experimental results on the hotspot detection problem show that our two-stage classification method achieves the best prediction accuracy and reduces false alarms significantly. ? 2021 IEEE.
Subjects
Classification (of information)
Convolutional neural networks
Data handling
Deterioration
Iterative methods
Data imbalance
Down sampling
Hotspot detections
Imbalance problem
Imbalanced data
Nanometer VLSI
Neural networks classifiers
Over sampling
Quality deteriorations
Two-stage classifiers
Errors
Type
conference paper