Recently, a lot of studies have been carried out to deal with various problems occurring in prediction or classification area using artificial intelligence networks. Data information processing is very significant to effectively cope with the design and realization of artificial intelligence networks architectures that handle small amounts of high-dimensional data sets.
Among these problems, One is the problem of being sensitive to noise and outliers due to the small amount of data for learning, and the other is the overfitting problem in which the parameters are fitted on learning data. In the previous studies, Generally, data information processing is done through dimensionality reduction algorithms such as principal component analysis(PCA) or linear discriminant analysis(LDA).
In this study, we propose a design methodology related to classifiers through data generated by generative networks to cope with small amounts of high-dimensional data classification problems.
The proposed methodology is to design convolutional variational autoencoder (CVAE) and deep convolutional generative adversarial networks (DCGAN) structures to newly generate 1-dimensional and 2-dimensional fake data sets from real data sets. And then fuzzy clustering methods-based radial base function neural network (FCM-based RBFNN) as well as convolutional neural networks (CNN) structures are exploited as classifiers for the processing of small amounts of high-dimensionality data.
1-dimensional and 2-dimensional fake data sets are generated with the aid of individual generative networks as follows:
The fake data sets generated by CVAE as well as DCGAN showed similar distributions to actual data in some classes from the viewpoint of data characteristics and patterns.
The experimental results for comparison between the performance of the proposed pattern classifiers and the existing pattern classifiers are as follows:
The entire classification performance of FCM-based RBFNN pattern classifiers is decreased when using generated fake data sets but, the performance is partially enhanced in some classes. The classification performance of CNN pattern classifiers is decreased partially in some classes but, has been significantly improved from the point of overall classification performance.
The total experimental results demonstrate that the generative network could partially enhance pattern classifier learning by generating fake data sets containing the characteristic patterns of real data sets.