CODES: Efficient Incremental Semi-Supervised Classification Over Drifting and Evolving Social Streams

Abstract

Classification over data streams is a crucial task of explosive social stream mining and computing. Efficient learning techniques provide high-quality services in the aspect of content distribution and event browsing. Due to the concept drift and concept evolution in data streams, the classification performance degrades drastically over time. Many existing methods utilize supervised and unsupervised learning strategies. However, supervised strategies require labeled emerging records to update the classifiers, which is unfeasible to work in the practical social stream applications. Although unsupervised strategies are popularly applied to detect concept evolution, it takes tremendous run-time computation cost to run online clustering. To this end, in this paper, we address these major challenges of social stream classification by proposing an efficient incremental semi-supervised classification method named CODES (Classification Over Drifting and Evolving Stream). The proposed CODES method consists of an efficient incremental semi-supervised learning module and a dynamic novelty threshold update module. Thus, in the drifting and evolving social streams, CODES is able to provide: 1) semi-supervised learning ability to eliminate dependency on the labels of emerging records; 2) fast incremental learning with real-time update ability to tackle concept drift; 3) efficient novel class detection ability to tackle concept evolution. Extensive experiments are conducted on several real-world datasets. The results indicate a higher performance than several state-of-the-art methods. CODES achieves efficient learning performance over drifting and evolving social streams, which improves practical significance in the real-world social stream applications. INDEX TERMS Social stream, incremental learning, semi-supervised learning, extreme learning machine.

Skewed Evolving Data Streams Classification with Actionable Knowledge Extraction using Data Approximation and Adaptive Classification Framework

“…In [31], an efficient incremental semi-supervised classification model called Classification Over Drifting and Evolving Streams (CODES). The CODES consists of an efficient incremental semi-supervised learning module and a dynamic novelty threshold update module.…”

“…The streaming data classification to address the class imbalance problem had studied in [21][22][23][24][25][26][27][28][29][30]. The streaming data classification with addressing the concept drift had been proposed in studies in [31][32][33][34][35]. And the stream data classification with concept drift and the class imbalance had studied in [36][37][38][39].…”

“…• Although idea drift and class imbalance have received considerable attention [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35], the combined solutions received less attention. Recently explored joint solutions in [36][37][38][39] are insufficient for the skewed dynamic data stream classification problem.…”

“…• Major solutions proposed for stream data learning and classification were based on a sliding windowbased mechanism which leads to poor stream data mining performances with higher costs due to duplicate chunks caused by moving sliding windows. • Data redundancy is a significant challenge for the existing data stream classification solutions [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

GNEA: A Graph Neural Network with ELM Aggregator for Brain Network Classification

“…e aggregation performance is boosted by an aggregator based on extreme learning machine (ELM) [19,20]. e extremely fast training speed and good generalization performance of ELM have been proven in various applications, for example, time-series learning [21][22][23], text mining [24,25], biomedical data analysis [26][27][28][29], graph classification [29,30], and game strategy [31]. e ELM aggregator learns a more complex aggregation function than those of other aggregators, which provides an extremely fast learning speed and a powerful aggregation ability.…”