Browsing by Subject "Data stream mining"

Now showing 1 - 3 of 3

Open Access
A broad ensemble learning system for drifting stream classification
(Institute of Electrical and Electronics Engineers, 2023-08-21) Bakhshi, Sepehr; Ghahramanian, Pouya; Bonab, H.; Can, Fazlı
In a data stream environment, classification models must effectively and efficiently handle concept drift. Ensemble methods are widely used for this purpose; however, the ones available in the literature either use a large data chunk to update the model or learn the data one by one. In the former, the model may miss the changes in the data distribution, while in the latter, the model may suffer from inefficiency and instability. To address these issues, we introduce a novel ensemble approach based on the Broad Learning System (BLS), where mini chunks are used at each update. BLS is an effective lightweight neural architecture recently developed for incremental learning. Although it is fast, it requires huge data chunks for effective updates and is unable to handle dynamic changes observed in data streams. Our proposed approach, named Broad Ensemble Learning System (BELS), uses a novel updating method that significantly improves best-in class model accuracy. It employs an ensemble of output layers to address the limitations of BLS and handle drifts. Our model tracks the changes in the accuracy of the ensemble components and reacts to these changes. We present our mathematical derivation of BELS, perform comprehensive experiments with 35 datasets that demonstrate the adaptability of our model to various drift types, and provide its hyperparameter, ablation, and imbalanced dataset performance analysis. The experimental results show that the proposed approach outperforms 10 state-of-the-art baselines, and supplies an overall improvement of 18.59% in terms of average prequential accuracy.
Open Access
A novel neural ensemble architecture for on-the-fly classification of evolving text streams
(Association for Computing Machinery (ACM) , 2024) Ghahramanian, Pouya; Bakhshi, Sepehr; Bonab, Hamed; Can, Fazlı
We study on-the-fly classification of evolving text streams in which the relation between the input data and target labels changes over time-i.e., "concept drift." These variations decrease the model's performance, as predictions become less accurate over time and they necessitate a more adaptable system. While most studies focus on concept drift detection and handling with ensemble approaches, the application of neural models in this area is relatively less studied. We introduce Adaptive Neural Ensemble Network (AdaNEN), a novel ensemble-based neural approach, capable of handling concept drift in data streams. With our novel architecture, we address some of the problems neural models face when exploited for online adaptive learning environments. Most current studies address concept drift detection and handling in numerical streams, and the evolving text stream classification remains relatively unexplored. We hypothesize that the lack of public and large-scale experimental data could be one reason. To this end, we propose a method based on an existing approach for generating evolving text streams by introducing various types of concept drifts to real-world text datasets. We provide an extensive evaluation of our proposed approach using 12 state-of-the-art baselines and 13 datasets. We first evaluate concept drift handling capability of AdaNEN and the baseline models on evolving numerical streams; this aims to demonstrate the concept drift handling capabilities of our method on a general spectrum and motivate its use in evolving text streams. The models are then evaluated in evolving text stream classification. Our experimental results show that AdaNEN consistently outperforms the existing approaches in terms of predictive performance with conservative efficiency.
Open Access
BELS: a broad ensemble learning system for data stream classification
(2021-12) Bakhshi, Sepehr
Data stream classification has become a major research topic due to the increase in temporal data. One of the biggest hurdles of data stream classification is the development of algorithms that deal with evolving data, also known as concept drifts. As data changes over time, static prediction models lose their validity. Adapting to concept drifts provides more robust and better performing models. The Broad Learning System (BLS) is an effective broad neural architecture recently developed for incremental learning. BLS cannot provide instant response since it requires huge data chunks and is unable to handle concept drifts. We propose a Broad Ensemble Learning System (BELS) for stream classification with concept drift. BELS uses a novel updating method that greatly improves bestin- class model accuracy. It employs a dynamic output ensemble layer to address the limitations of BLS. We present its mathematical derivation, provide comprehensive experiments with 11 datasets that demonstrate the adaptability of our model, including a comparison of our model with BLS, and provide parameter and robustness analysis on several drifting streams, showing that it statistically significantly outperforms seven state-of-the-art baselines. We show that our proposed method improves on average 44% compared to BLS, and 29% compared to other competitive baselines.