Turnitin
降AI改写
早检测系统
早降重系统
Turnitin-UK版
万方检测-期刊版
维普编辑部版
Grammarly检测
Paperpass检测
checkpass检测
PaperYY检测
Machine learning for streaming data: state of the art, challenges, and opportunities
Incremental learning, online learning, and data stream learning are terms commonly
associated with learning algorithms that update their models given a continuous influx of …
associated with learning algorithms that update their models given a continuous influx of …
A survey on feature drift adaptation: Definition, benchmark, challenges and future directions
Data stream mining is a fast growing research topic due to the ubiquity of data in several real-
world problems. Given their ephemeral nature, data stream sources are expected to …
world problems. Given their ephemeral nature, data stream sources are expected to …
Rotor angle stability prediction of power systems with high wind power penetration using a stability index vector
This paper proposes a methodology for predicting online rotor angle stability in power
system operation under significant contribution from wind generation. First, a novel algorithm …
system operation under significant contribution from wind generation. First, a novel algorithm …
Original Research Article Stream learning under concept and feature drift: A literature survey
Stream data learning is an emerging machine learning topic, and it has many challenges.
One of its challenges is the dynamic behavior or changes in the environment which leads to …
One of its challenges is the dynamic behavior or changes in the environment which leads to …
A general framework based on dynamic multi-objective evolutionary algorithms for handling feature drifts on data streams
This paper proposes a new and efficient framework to deal with the classification of data
streams when exhibiting feature drifts. The first building block of the framework is a dynamic …
streams when exhibiting feature drifts. The first building block of the framework is a dynamic …
On dynamic feature weighting for feature drifting data streams
The ubiquity of data streams has been encouraging the development of new incremental
and adaptive learning algorithms. Data stream learners must be fast, memory-bounded, but …
and adaptive learning algorithms. Data stream learners must be fast, memory-bounded, but …
Dynamic feature weighting for data streams with distribution-based log-likelihood divergence
X Wang, H Wang, D Wu - Engineering Applications of Artificial Intelligence, 2022 - Elsevier
Data streams are expected to undergo changes in data distribution, a phenomenon called
concept drift. Another closely related phenomenon is the feature drift of data streams …
concept drift. Another closely related phenomenon is the feature drift of data streams …
A survey on feature drift adaptation
Mining data streams is of the utmost importance due to its appearance in many real-world
situations, such as: sensor networks, stock market analysis and computer networks intrusion …
situations, such as: sensor networks, stock market analysis and computer networks intrusion …
Efficient computation of counterfactual explanations and counterfactual metrics of prototype-based classifiers
The increasing use of machine learning in practice and legal regulations like EU's GDPR
cause the necessity to be able to explain the prediction and behavior of machine learning …
cause the necessity to be able to explain the prediction and behavior of machine learning …
Near real-time intrusion alert aggregation using concept-based learning
Intrusion detection systems generate a large number of streaming alerts. It can be
overwhelming for analysts to quickly and effectively find related alerts stemmed from …
overwhelming for analysts to quickly and effectively find related alerts stemmed from …