# Process-Oriented Stream Classification Pipeline: A Literature Review
## 7 Current and Future Research Directions
### Current Work in the Field
#### 7.2.3 Stream Classification Algorithms and Architectures  
The word cloud offers words such as \"deep\", \"neural\", \"network\", \"ensemble\", and \"models\", which indicate that researchers investigate classification algorithm development. Examples are further development on dynamic ensemble selection methods under evolving data streams [192], or adaptions of the _ARF_ model [193]. The modified ARF algorithm combines the original adaptive forest approach with the compressed sensing method. By this, the algorithm tackles the problem of high-dimensional data. Compressed sensing is used as an internal preprocessing step and ensures that sparse data is compressed and only a smaller number of crucial and low coherent features is used for further actions. The authors evaluated the approach using the MOA framework and the contained datasets. They also created three Twitter datasets with the MOA Tweet reader to analyze the algorithm's performance, specifically on high-dimensional data. They tested the approach regarding its classification accuracy and performance indicators such as memory utilization and computation time. Using the complete feature information and ARF with the dimension reduction of compressed sensing showed comparable performance with lower computational costs. It outperforms state-of-the-art algorithms such as Hoeffding Adaptive Trees, Self-Adjusting Memory kNN, and Naive Bayes [193]. Ref. [194] propose an algorithm to handle high-dimensional data with seasonal concept drift. They adapt to the AODE classifier based on the idea of a classical Naive Bayes, relaxing the independence assumption of features. They include time as a powerful feature that calculates correlation to classes and other features. The authors evaluated the algorithm on two text-based real-world data sets against nine state-of-the-art tree and frequency-based algorithms and achieved consistently better results.  
Aligned with the increase in computational resources, various works explore the use of deep neural networks in the context of data stream classification. Of course, CNN's are constantly being developed and improved. For instance, in image analysis, it can be helpful to combine several network structures to process different information sources of an image. For example, ref. [15] uses a two-strand CNN approach, where one network operates on two extracted feature bases that can be obtained from an RGB color-coded image. The authors evaluated their approach to 3D-sign recognition data sets and demonstrated an improvement in classification performance compared to state-of-the-art models in the field.  
Ref. [195] introduces a BERT-based deep learning model for streaming NLP tasks. Concretely, their approach is based on an adaptive network that outperforms state-of-the-art approaches, especially regarding imbalanced data distributions. Moreover, ref. [196] introduces a Recurrent Neural Network for astronomical lightcurve classification with a focus on imbalanced data streams. The authors do not apply sampling strategies to the telescope data but modify the algorithm so that the bias towards the majority class is down-weighted. Further, they include additional contextual information--e.g., distance to other galaxies--into the classification setting. They demonstrate that the approach outperforms algorithms without a weighted function and additional contextual information.