Abstract
This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network. Afterwards, the linear model transforms the calculated output of the deep stacked sparse auto-encoder to a value close to the anticipated output. This simple transformation increases the overall data classification performance of the stacked sparse auto-encoder architecture. The PSO algorithm allows the estimation of the parameters of the linear model in a metaheuristic policy. The proposed framework is validated by using three public datasets, which present promising results when compared with the current literature. Furthermore, the framework can be applied to any data classification problem by considering minor updates such as altering some parameters including input features, hidden neurons and output classes.
Highlights
Deep learning (DL) is a new paradigm of neural networks, which is employed in different fields such as image classification and recognition, medical imaging and robotics etc
The parameters calculated to improve the performance of the proposed framework are: “True Positive Rate” (Recall), “True Negative Rate” (TNR), “positive predictive value” (Precision), “negative predictive value” (NPV), “false positive rate” (FPR), “false discovery rate” (FDR), “miss rate”
This paper proposes a framework for data classification problems
Summary
Deep learning (DL) is a new paradigm of neural networks, which is employed in different fields such as image classification and recognition, medical imaging and robotics etc. The deep auto-encoder (DAE) is a popular deep learning technique and has been recently adapted to various applications in different fields [1,2,3,4]. Bhatkoti and Paul propose a new framework for Alzheimer’s disease diagnosis based on deep learning and the KSA algorithm. In this application, the results of the modified approach are compared to the non-modified k-sparse method. Tong et al present a software defect prediction application by using the advantages of stacked denoising auto-encoders (SDAEs) and a two-stage ensemble (TSE). A new ensemble learning method, TSE, is proposed to predict the label imbalance problem. The proposed method is trained and tested by using 12 NASA benchmark test data to show the effectiveness of the SDAEsTSE system, which is significantly effective for software defect prediction [7]
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