Class imbalance problems have been reported as a major issue in various applications. Classification becomes further complicated when an imbalance occurs in time series data sets. To address time series data, it is necessary to consider their characteristics (i.e., high dimensionality, high correlations, and multimodality). Oversampling is a well-known approach for addressing this problem; however, such an approach does not appropriately consider the characteristics of time series data. This paper addresses these limitations by presenting a model-based oversampling approach, a boundary-focused generative adversarial network (BFGAN). The proposed BFGAN employs a specifically designed additional label for reflecting the importance of a sample's position in data space. Furthermore, the BFGAN generates artificial samples after taking into consideration a sample's multimodality and importance by using a suitable modified GAN structure. We present empirical results that reveal a significant improvement in the quality of the generated data when the proposed BFGAN is used as an oversampling algorithm for an imbalanced multimodal time series data set.

We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At each step of the local search, a hybrid search method combining simulated annealing with a greedy algorithm was adopted. The proposed PSAGA aims to achieve both the efficiency of parallel search and the effectiveness of a more exhaustive search. The Bayesian Dirichlet equivalence metric was used to determine an optimal structure for PSAGA. The proposed PSAGA was evaluated on seven well-known Bayesian network benchmarks generated at random. We first conducted experiments to evaluate the computational time performance of the proposed parallel search. We then compared PSAGA with existing variants of simulated annealing-based algorithms to evaluate the quality of the learned structure. Overall, the experimental results demonstrate that the proposed PSAGA shows better performance than the alternatives in terms of computational time and accuracy.