More Like this

1. Unsupervised Drift Detection on High-speed Data Streams

   https://doi.org/10.1109/bigdata50022.2020.9377880  

   Souza, Vinicius M. ; Chowdhury, Farhan A. ; Mueen, Abdullah ( December 2020 , 2020 IEEE International Conference on Big Data (Big Data))

   null (Ed.)

   Changes in data distribution of streaming data (i.e., concept drifts), constitute a central issue in online data mining. The main reason is that these changes are responsible for outdating stream learning models, reducing their predictive performance over time. A common approach adopted by real-time adaptive systems to deal with concept drifts is to employ detectors that indicate the best time for updates. However, an unrealistic assumption of most detectors is that the labels become available immediately after data arrives. In this paper, we introduce an unsupervised and model-independent concept drift detector suitable for high-speed and high-dimensional data streams in realistic scenarios with the scarcity of labels. We propose a straightforward two-dimensional representation of the data aiming faster processing for detection. We develop a simple adaptive drift detector on this visual representation that is efficient for fast streams with thousands of features and is accurate as existing costly methods that perform various statistical tests. Our method achieves better performance measured by execution time and accuracy in classification problems for different types of drifts, including abrupt, oscillating, and incremental. Experimental evaluation demonstrates the versatility of the method in several domains, including astronomy, entomology, public health, political science, and medical science. 

   more » « less
2. Efficient unsupervised drift detector for fast and high-dimensional data streams

   https://doi.org/10.1007/s10115-021-01564-6  

   Souza, Vinicius M. ; Parmezan, Antonio R. ; Chowdhury, Farhan A. ; Mueen, Abdullah ( April 2021 , Knowledge and Information Systems)

   null (Ed.)

   Stream mining considers the online arrival of examples at high speed and the possibility of changes in its descriptive features or class definitions compared with past knowledge (i.e., concept drifts). The fast detection of drifts is essential to keep the predictive model updated and stable in changing environments. For many applications, such as those related to smart sensors, the high number of features is an additional challenge in terms of memory and time for stream processing. This paper presents an unsupervised and model-independent concept drift detector suitable for high-speed and high-dimensional data streams. We propose a straightforward two-dimensional data representation that allows the faster processing of datasets with a large number of examples and dimensions. We developed an adaptive drift detector on this visual representation that is efficient for fast streams with thousands of features and is accurate as existing costly methods that perform various statistical tests considering each feature individually. Our method achieves better performance measured by execution time and accuracy in classification problems for different types of drifts. The experimental evaluation considering synthetic and real data demonstrates the method’s versatility in several domains, including entomology, medicine, and transportation systems. 

   more » « less
3. A Clustering-based framework for Classifying Data Streams

   https://doi.org/10.24963/ijcai.2021/448  

   Yan, Xuyang ; Homaifar, Abdollah ; Sarkar, Mrinmoy ; Girma, Abenezer ; Tunstel, Edward ( July 2021 , International Joint Conferences on Artificial Intelligence Organization)

   The non-stationary nature of data streams strongly challenges traditional machine learning techniques. Although some solutions have been proposed to extend traditional machine learning techniques for handling data streams, these approaches either require an initial label set or rely on specialized design parameters. The overlap among classes and the labeling of data streams constitute other major challenges for classifying data streams. In this paper, we proposed a clustering-based data stream classification framework to handle non-stationary data streams without utilizing an initial label set. A density-based stream clustering procedure is used to capture novel concepts with a dynamic threshold and an effective active label querying strategy is introduced to continuously learn the new concepts from the data streams. The sub-cluster structure of each cluster is explored to handle the overlap among classes. Experimental results and quantitative comparison studies reveal that the proposed method provides statistically better or comparable performance than the existing methods.

    

   more » « less
4. Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation

   Justesen, Niels ; Rodriguez Torrado, Ruben ; Bontrager, Philip ; Khalifa, Ahmed ; Togelius, Julian ; Risi, Sebastian ( January 2018 , NeurIPS Workshop on Deep Reinforcement Learning)

   Deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when neural networks are trained in a fixed environment, such as a single level in a video game, they will usually overfit and fail to generalize to new levels. When RL models overfit, even slight modifications to the environment can result in poor agent performance. This paper explores how procedurally generated levels during training can increase generality. We show that for some games procedural level generation enables generalization to new levels within the same distribution. Additionally, it is possible to achieve better performance with less data by manipulating the difficulty of the levels in response to the performance of the agent. The generality of the learned behaviors is also evaluated on a set of human-designed levels. The results suggest that the ability to generalize to human-designed levels highly depends on the design of the level generators. We apply dimensionality reduction and clustering techniques to visualize the generators’ distributions of levels and analyze to what degree they can produce levels similar to those designed by a human. 

   more » « less
5. Randomized error removal for online spread estimation in data streaming

   https://doi.org/10.14778/3447689.3447707  

   Wang, Haibo ; Ma, Chaoyi ; Odegbile, Olufemi O ; Chen, Shigang ; Peir, Jih-Kwon ( February 2021 , Proceedings of the VLDB Endowment)

   null (Ed.)

   Measuring flow spread in real time from large, high-rate data streams has numerous practical applications, where a data stream is modeled as a sequence of data items from different flows and the spread of a flow is the number of distinct items in the flow. Past decades have witnessed tremendous performance improvement for single-flow spread estimation. However, when dealing with numerous flows in a data stream, it remains a significant challenge to measure per-flow spread accurately while reducing memory footprint. The goal of this paper is to introduce new multi-flow spread estimation designs that incur much smaller processing overhead and query overhead than the state of the art, yet achieves significant accuracy improvement in spread estimation. We formally analyze the performance of these new designs. We implement them in both hardware and software, and use real-world data traces to evaluate their performance in comparison with the state of the art. The experimental results show that our best sketch significantly improves over the best existing work in terms of estimation accuracy, data item processing throughput, and online query throughput. 

   more » « less