A New Generalized Deep Learning Framework Combining Sparse Autoencoder and Taguchi Method for Novel Data Classification and Processing

Abstract

Deep autoencoder neural networks have been widely used in several image classification and recognition problems, including hand-writing recognition, medical imaging, and face recognition. The overall performance of deep autoencoder neural networks mainly depends on the number of parameters used, structure of neural networks, and the compatibility of the transfer functions. However, an inappropriate structure design can cause a reduction in the performance of deep autoencoder neural networks. A novel framework, which primarily integrates the Taguchi Method to a deep autoencoder based system without considering to modify the overall structure of the network, is presented. Several experiments are performed using various data sets from different fields, i.e., network security and medicine. The results show that the proposed method is more robust than some of the well-known methods in the literature as most of the time our method performed better. Therefore, the results are quite encouraging and verified the overall performance of the proposed framework.

A Q-Learning-Based Approach for Simple and Multi-Agent Systems

“…Stacked sparse auto-encoder is a type of deep neural network involving stacking sparse auto-encoders, and a classifier is regularly used as the final layer for mainly classification or regression problems [18]. This model has not been applied in such a problem which encourages authors to employ the technique into the current problem.…”

“…Accordingly, the first auto-encoders are trained by utilizing an unsupervised training method [18]. Fundamentally, the output of the first sparse auto-encoder is considered as an input to the second one, and the output of second auto-encoders becomes an input to the classifier as shown in the corresponding figure.…”

“…Here X indicates the number of hidden layers, n signifies observation numbers, and k shows hidden layers [18].…”

“…Sparsity Regularization is on the other hand can be defined as follows: where anticipated value is represented by ρ,ρ i denotes the average output activation of each neuron }i, } and "KL" is the function that evaluates the variance between two probabilities distribution over the same data. The details of those equations can be seen in [18].…”

Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques

“…The stacked sparse autoencoder (SSAE) is multiple layers of sparse autoencoders neural network, which is used as an unsupervised feature extraction method, and the Taguchi Method is employed for parameter optimization. This novel framework is tested with different experimental data sets like DDoS Detection, IDS Attack, Epileptic Seizure Recognition, and handwritten digit classification problem (Karim et al, 2018). There was a proposal of deep autoencoder architecture for medical data preprocessing, and for classification, the softmax classifier layer is trained.…”