Browsing by Subject "Problem transformation"

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Open Access
Binary transformation method for multi-label stream classification
(Association for Computing Machinery, 2022-10-17) Gülcan, Ege Berkay; Ecevit, Işın Su; Can, Fazlı
Data streams produce extensive data with high throughput from various domains and require copious amounts of computational resources and energy. Many data streams are generated as multi-labeled and classifying this data is computationally demanding. Some of the most well-known methods for Multi-Label Stream Classification are Problem Transformation schemes; however, previous work on this area does not satisfy the efficiency demands of multi-label data streams. In this study, we propose a novel Problem Transformation method for Multi-Label Stream Classification called Binary Transformation, which utilizes regression algorithms by transforming the labels into a continuous value. We compare our method against three of the leading problem transformation methods using eight datasets. Our results show that Binary Transformation achieves statistically similar effectiveness and provides a much higher level of efficiency.
Open Access
Prioritized binary transformation method for efficient multi-label classification of data streams with many labels
(Association for Computing Machinery, 2024-10-21) Yıldırım, Onur; Bakhshi, Sepehr; Can, Fazlı
Real-time data processing systems generate huge amounts of data that need to be classified. The volume, variety, velocity, and veracity (uncertainty) of this data necessitate new approaches and the adaptation of existing classification methods. Moreover, the arriving data can belong to more than one class at the same time. As the number of labels grows larger, a significant portion of the multi-label data stream classification methods become computationally inefficient. We propose a novel online approach: the Prioritized Binary Transformation (PBT) method, which can classify data with large numbers of labels by ordering the labels using Principal Component Analysis (PCA) within a fixed-size window. This order is then used to transform the label vectors for classification. We perform an empirical analysis on 12 datasets and compare PBT to four prominent baselines using four evaluation metrics. PBT achieves the best average ranking in three of the four evaluation metrics. Moreover, we investigate efficiency under average execution time per data item and memory consumption where PBT achieves second and first average rankings, respectively. © 2024 Owner/Author.