# A Pilot Study of Stacked Autoencoders for Ship Mode Classification
## 2 Related Works  
In the past several years, many studies have applied autoencoder models to solve practical problems. An autoencoder [18] is a learning neural network model that approximates input and output values to reconstruct them the same, and the main purpose of the autoencoder is to learn informative representations of data using an unsupervised method. Types of autoencoders include the stacked autoencoder, sparse autoencoder, denoising autoencoder, contractive autoencoder, and variational autoencoder [19]. The stacked autoencoder can learn efficiently to create robust features from training data. Research has been conducted on the benefits of stacked autoencoders to solve their problems. [PERSON] et al. [20] used a stacked autoencoder model to classify human emotional data and achieved good results in categorizing human emotions with a spectrogram dataset. [PERSON]'s approach was able to produce better results compared to traditional methods, which could not distinguish between happy and angry people. [PERSON] et al. [21] compared different conventional neural networks, support vector machines, and stacked autoencoders on the recognition of continuous phase frequency-shift keying under carrier frequency offset, noisy, and fast-fading channel conditions with the based model. In this study, the three features selected for recognition were the approximate entropy of the received signal, the approximate entropy of the received signal's phase, and the approximate entropy of the instantaneous frequency of the received signal. It was found that the stacked autoencoder performed better than support vector machines and traditional neural networks; a stacked autoencoder model can give a better accuracy result for most signal to noise ratios. [PERSON] et al. [22] proposed a stacked autoencoder model to reduce complexity and processing time for detecting epilepsy. The model classified epileptic data into normal, ictal, and preictal. He selected machine learning algorithms such as Bayes Net, Naive Bayes, multilayer perceptron, radial basis function neural networks, and the C4.5 decision tree classifier as comparison models. He proved that the stacked autoencoder model had the best performance score with the least processing time. [PERSON] et al. [23] suggested using a cascade of two types of networks, stacked autoencoders and extreme learning machines, for multi-label classification to enhance a stacked autoencoder's performance. The proposed model was compared with eleven other algorithms with seven datasets. However, she claimed that the model had promising performance. [PERSON] et al. [24] introduced a stacked sparse autoencoder model to integrate feature extraction and classification processes. The model uses denoising and a dropout technique to enhance feature extraction performance and prevent overfitting. It was proven that the model produced a better output than conventional models. [PERSON] [25] built a stacked autoencoder classification model for heart sound analysis. Traditional methods use data transforms to get the S1 and S2 segments of heart sounds. [PERSON]'s novel approach was to utilize only a stacked autoencoder model to get segments of heart sounds for direct classification. This model was compared with conventional algorithms. The proposed model's performance was similar to prior models. According to this study, a stacked autoencoder can be used in the medical field with efficient and effective classification results. [PERSON] et al. [26] compared a proposed stacked autoencoder model with seven previously established algorithms using ten datasets to find the key genes for cancer. Traditional gene selection approaches using statistical or feature selection methods have accuracy problems. However, the proposed stacked autoencoder-based framework outperformed conventional methods in this study. [PERSON] et al. [27] introduced a reduced noise-autoencoder for solving the problem of imbalanced data in genomic datasets.