A-DBNF: adaptive deep belief network framework for regression and classification tasks

摘要

Many machine learning methods and models have been proposed for multivariate data regression and classification in recent years. Most of them are supervised learning methods, which require a large number of labeled data. Moreover, current methods need exclusive human labor and supervision to fine-tune the model hyperparameters. In this paper, we propose an adaptive deep belief network framework (A-DBNF) that can adapt to different datasets with minimum human labor. The proposed framework employs a deep belief network (DBN) to extract representative features of the datasets in the unsupervised learning phase and then fine-tune the network parameters by using few labeled data in the supervised learning phase. We integrate the DBN model with a genetic algorithm (GA) to select and optimize the model hyperparameters and further improve the network performance. We validate the performance of the proposed framework on several benchmark datasets, comparing the regression and classification accuracy with state-of-the-art methods. A-DBNF showed a noticeable performance improvement on three regression tasks using only 40–50% of labeled data. Our model outperformed most of the related methods in classification tasks by using 23–48% of labeled data.