# 6G Cognitive Information Theory: A Mailbox Perspective
## 6 Data Life Cycle Extensions for Information Value
### Data Fusion for Data Analysis
#### 6.4.2 Data-Fusion Technologies
As an example, for classification problems, multiple classifiers are established, and then methods, such as adopting a voting mechanism, are used to avoid unreliable results. In this way the reliability and accuracy of the algorithm can be effectively improved. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Data-Fusion Technology** & **Description** & **Fusion Target** & **Literature** \\ \hline Regression & Linear, polynomial, logical, stepwise, exponential, multiple adaptive regression spline & Strengthening prediction and decision-making & [53; 54] \\ \hline Classification & K-proximity, proximity, decision tree, Bayes, Support vector machine, Learning vector quantization, self-organizing mapping, local weighted learning & Multi-source data integration, decision-making & [55; 56] \\ \hline Bayesian network & Bayes, Gauss, polynomial, average dependence estimation, Bayesian belief, Bayesian network & Multi-source data integration, strengthening decision-making and estimation & [57; 58] \\ \hline Decision tree & Decision tree, random forest, classification and regression number, ID3, automatic interaction detection of card room & Strengthening prediction and decision-making & [59] \\ \hline \end{tabular}
\end{table} 
Table 2: Data fusion technology based on machine learning. _(2) Data fusion and analysis based on deep learning:_  
Deep learning can learn data characteristics, and the performance is more prominent when processing unstructured data, such as image data, text data, and voice data. In heterogeneous IoT, fusion and analysis are performed on a large amount of structured and unstructured data, and the corresponding pattern can be established. 1) Artificial neural network (ANN): An ANN is a mathematical model abstracted with inspiration from the structure and function of the human brain. It has been widely used in the field of pattern recognition, image processing, intelligent control, combinatorial optimization, robotics, and expert systems. Specifically, there are many similarities between an ANN and the human brain. The basic structure has a set of connected input and output units with several hidden layers in the middle, and the nodes between each connection are associated with weights. In the training and learning stages, these weights can be adjusted according to the class label of the predicted input tuples and the correct class label. 2) Convolutional neural network (CNN): A CNN is a kind of feedforward neural network. Compared with a traditional feedforward neural network, a method of weight sharing is adopted to reduce the number of weights in the network and reduce the complexity of the computing. The structure of this network is similar to that of biological neural networks. As a supervised learning method, CNN is widely used in voice recognition and image detection and recognition. CNN is a good choice for data-fusion technologies that need to understand data, describe characteristics of a data set, discover relationships and patterns existing in the data, establish models, and make predictions. 3) Recurrent neural network (RNN): Each time point of the RNN corresponds to a three-layer ANN, and thus the training of the RNN is similar to that of the ANN. RNN is a neural network where the data characteristics of a time sequence are considered, where information remembered from the past can be used in the computation of output data at