Browsing by Subject "Drift detection"

Now showing 1 - 4 of 4

Open Access
Concept learning using one-class classifiers for implicit drift detection in evolving data streams
(Springer, 2021-06) Gözüaçık, Ömer; Can, Fazli
Data stream mining has become an important research area over the past decade due to the increasing amount of data available today. Sources from various domains generate a near-limitless volume of data in temporal order. Such data are referred to as data streams, and are generally nonstationary as the characteristics of data evolves over time. This phe nomenon is called concept drift, and is an issue of great importance in the literature, since it makes models obsolete by decreasing their predictive performance. In the presence of concept drift, it is necessary to adapt to change in data to build more robust and efective classifers. Drift detectors are designed to run jointly with classifcation models, updating them when a signifcant change in data distribution is observed. In this paper, we present an implicit (unsupervised) algorithm called One-Class Drift Detector (OCDD), which uses a one-class learner with a sliding window to detect concept drift. We perform a compre hensive evaluation on mostly recent 17 prevalent concept drift detection methods and an adaptive classifer using 13 datasets. The results show that OCDD outperforms the other methods by producing models with better predictive performance on both real-world and synthetic datasets.
Open Access
Implicit concept drift detection for multi-label data streams
(2022-01) Gülcan, Ege Berkay
Many real-world applications adopt multi-label data streams as the need for algo-rithms to deal with rapidly generated data increases. For such streams, changes in data distribution, also known as concept drift, cause the existing classification models to rapidly lose their effectiveness. To assist the classifiers, we propose a novel algorithm called Label Dependency Drift Detector (LD3), an implicit (un-supervised) concept drift detector using label dependencies within the data for multi-label data streams. Our study exploits the dynamic temporal dependencies between labels using a label influence ranking method, which leverages a data fusion algorithm and uses the produced ranking to detect concept drift. LD3 is the first unsupervised concept drift detection algorithm in the multi-label classification problem area. In this study, we perform an extensive evaluation of LD3 by comparing it with 14 prevalent supervised concept drift detection algorithms that we adapt to the problem area using 12 datasets and a baseline classifier. The results show that LD3 provides between 19.8% and 68.6% better predictive performance than comparable detectors on both real-world and synthetic data streams.
Open Access
Unsupervised concept drift detection for multi-label data streams
(Springer, 2022-07-17) Gülcan, Ege Berkay; Can, Fazlı
Many real-world applications adopt multi-label data streams as the need for algorithms to deal with rapidly changing data increases. Changes in data distribution, also known as concept drift, cause existing classification models to rapidly lose their effectiveness. To assist the classifiers, we propose a novel algorithm called Label Dependency Drift Detector (LD3), an unsupervised concept drift detector using label dependencies within the data for multi-label data streams. Our study exploits the dynamic temporal dependencies between labels using a label influence ranking method, which leverages a data fusion algorithm and uses the produced ranking to detect concept drift. LD3 is the first unsupervised concept drift detection algorithm in the multi-label classification problem area. In this study, we perform an extensive evaluation of LD3 by comparing it with 14 prevalent supervised concept drift detection algorithms that we adapt to the problem area using 15 datasets and a baseline classifier. The results show that LD3 provides between 16.9 and 56% better predictive performance than comparable detectors on both real-world and synthetic data streams. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
Open Access
Unsupervised concept drift detection with a discriminative classifier
(Association for Computing Machinery, 2019) Gözüaçık, Ömer; Büyükçakır, Alican; Bonab, H.; Can, Fazlı
In data stream mining, one of the biggest challenges is to develop algorithms that deal with the changing data. As data evolve over time, static models become outdated. This phenomenon is called concept drift, and it is investigated extensively in the literature. Detecting and subsequently adapting to concept drifts yield more robust and better performing models. In this study, we present an unsupervised method called D3 which uses a discriminative classifier with a sliding window to detect concept drift by monitoring changes in the feature space. It is a simple method that can be used along with any existing classifier that does not intrinsically have a drift adaptation mechanism. We experiment on the most prevalent concept drift detectors using 8 datasets. The results demonstrate that D3 outperforms the baselines, yielding models with higher performances on both real-world and synthetic datasets.