Big data is important in knowledge manipulation, assessment, and prediction. However, extracting and analyzing knowledge through big database are complex because of imbalance data distribution that leads to wrong decisions and biased classification outputs. Hence, an effective and optimal big data classification approach is designed using the proposed Bird Swarm Deer Hunting Optimization-Deep Belief Network (BSDHO-based DBN) algorithm based on spark architecture that follows the master and slave nodes. The proposed BSDHO is obtained by combining Deer Hunting Optimization algorithm and Bird Swarm Algorithm. The developed model poses two nodes, namely slave and master node. The training data is initially given to the master node in the spark architecture to perform transformation of data. Here, the transformation of data is done with an exponential log kernel, and then selection of feature is done with sequential forward selecting for choosing suitable features for enhanced processing. Consequently, oversampling process is performed with Fuzzy K-Nearest Neighbor (Fuzzy KNN) in the slave node using selected features to manage imbalance data. Then, in master node, classification is done with Deep belief Network, and trained using developed Bird swarm Deer Hunting Optimization (BSDHO) algorithm. On the other hand, the test data is taken as input, and is fed to the slave node to perform data transformation. Then, the transformed data is given to the master node for classification based on the proposed BSDHO. At last, the training data and testing data output produced the classified output. The proposed BSDHO-based DBN provided enhanced outcomes with highest specificity of 97.92%, accuracy of 96.92%, and sensitivity of 96.9%.

大数据在知识操纵、评估和预测方面非常重要。然而，通过大数据库提取和分析知识是复杂的，因为数据分布不平衡会导致错误的决策和有偏见的分类输出。因此，基于遵循主从节点的 spark 架构，使用所提出的鸟群猎鹿优化-深度信念网络（基于 BSDHO 的 DBN）算法设计了一种有效且最佳的大数据分类方法。所提出的 BSDHO 是通过结合猎鹿优化算法和鸟群算法获得的。开发的模型包含两个节点，即从节点和主节点。训练数据最初被交给spark架构中的master节点进行数据的转换。这里，数据的转换是用指数对数核完成的，然后特征的选择是通过顺序前向选择来选择合适的特征进行增强处理的。因此，在从属节点中使用模糊 K 最近邻（模糊 KNN）执行过采样过程，使用选定的特征来管理不平衡数据。然后，在主节点中，使用深度信念网络进行分类，并使用开发的鸟群猎鹿优化（BSDHO）算法进行训练。另一方面，将测试数据作为输入，馈送到从节点进行数据转换。然后，将转换后的数据提供给主节点，以基于提议的 BSDHO 进行分类。最后，训练数据和测试数据输出产生分类输出。