The most pervasive segment of techniques in managing class imbalance in machine learning are re-sampling-based methods. The emergence of deep generative models for augmenting the size of the under-represented class, prompts one to review the question of the suitability of the model chosen for data augmentation with the metric selected for the-goodness-of classification. This work defines this suitability by using newly-sampled data points from each generative model first to the degree of parity, and studying classification performance on a large set of metrics. We extend the investigation to different proportions of augmented data points for identifying the sensitivity of the metric to the degree of imbalance, leading to the discovery of an optimum proportion against the metric. The models used are GAN, VAE and RBM and the metrics include Precision, Recall, F1-Score, AUC, G-Mean and Balanced Accuracy. We offer a comparison of these models with the established class of data synthesizing counterparts on the aforementioned metrics. Deep generative models outperform the state-of-the-art on 5 metrics on multiple datasets and also comprehensively surpass the baselines. This work thereby recommends the following model-metric mappings: VAE for high Precision and F1-Score, RBM for high Recall and GAN for high AUC, G-Mean and Balanced Accuracy under various recommended proportions of the minority class.
Abstract-Puzzles and board games represent several important classes of AI problems, but also represent difficult complexity classes. In this paper, we propose a deep learning based alternative to train a neural network model to find solution states of the popular puzzle game Sokoban. The network trains against a classical solver that uses theorem proving as the oracle of valid and invalid games states, in a setup that is similar to the popular adversarial training framework. Using our approach, we have been able to verify the validity of a Sokoban puzzle up to an accuracy of 99% on the test set. We have also been able to train our network to generate the next possible state of the puzzle board up to an accuracy of 99% on the validation set. We hope that through this approach, a trained neural network will be able to replace human experts and classical rule-based AI in generating new instances and solutions for such games.
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