A Dynamic Data-Driven Fine-Tuning Approach for Stacked Auto-Encoder Neural Network

Abstract

With the advent of the big data era, dynamic and real-time data have increased in both volume and varieties. It is a difficult task to achieve an accurate prediction results to rapidly dynamic changing data. The stacked auto-encoder is a neural network approach in machine learning for feature extraction. It attempts to model high-level abstractions and dimension reduction in data by using multiple processing layers. However, some of the common issues may occur during the implementation process of deep learning or neural network, such as input data having over-complicated dimension, and unable to execute in a dynamic environment. Therefore, it will be helpful if we combine dynamic data-driven concept with stacked auto-encoder neural network to obtain the dynamic data correlation or relationship between prediction results and actual data in a dynamic environment. This study applies the concept of dynamic data-driven to obtain the correlations between the prediction goals and numbers of different combination results. The methods of association analysis, sequence analysis, and stacked auto-encoder neural network are applied to design a dynamic data-driven system based on deep learning.