Results 31 to 40 of about 5,397,718 (323)
Rapidrift: Elementary Techniques to Improve Machine Learning-Based Malware Detection
Computers, 2023 Artificial intelligence and machine learning have become a necessary part of modern living along with the increased adoption of new computational devices.
Abishek Manikandaraja +2 more
doaj +1 more source
Learning from Ontology Streams with Semantic Concept Drift [PDF]
, 2017 Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream.
Chen, Huajun +3 more
core +4 more sources
CONDA-PM—A Systematic Review and Framework for Concept Drift Analysis in Process Mining
Algorithms, 2020 Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it.
Ghada Elkhawaga +4 more
doaj +1 more source
Concept drift estimation with graphical models
Information Sciences, 2022 This paper deals with the issue of concept-drift in machine learning in the context of high dimensional problems. In contrast to previous concept drift detection methods, this application does not depend on the machine learning model in use for a specific target variable, but rather, it attempts to assess the concept drift as an independent ...
Riso, Luigi, Guerzoni, Marco
openaire +2 more sources
DED: Drift Principle in Educational Evolved Data
Tikrit Journal of Pure Science, 2022 Clustering data streams is one of the prominent tasks of discovering hidden patterns in data streams. It refers to the process of clustering newly arrived data into continuously and dynamically changing segmentation patterns.
Ammar Thaher Yaseen Al Abd Alazeez
doaj +1 more source
Is It Overkill? Analyzing Feature-Space Concept Drift in Malware Detectors
2023 IEEE Security and Privacy Workshops (SPW), 2023 Concept drift is a major challenge faced by machine learning-based malware detectors when deployed in practice. While existing works have investigated methods to detect concept drift, it is not yet well understood regarding the main causes behind the ...
Zhi Chen +8 more
semanticscholar +1 more source
Tracking Drifting Concepts By Minimizing Disagreements [PDF]
Machine Learning, 1994 In this paper we consider the problem of tracking a subset of a domain (called the target) which changes gradually over time. A single (unknown) probability distribution over the domain is used to generate random examples for the learning algorithm and measure the speed at which the target changes. Clearly, the more rapidly the target moves, the harder
Helmbold, David P., Long, Philip M.
openaire +3 more sources
International journal of computer science and mobile computing, 2023 Credit card fraud is a significant concern for financial institutions and cardholders alike. As fraudulent activities become more sophisticated, traditional rule-based approaches struggle to keep up.
Oluwadare Samuel Adebayo +3 more
semanticscholar +1 more source
An Ensemble Extreme Learning Machine for Data Stream Classification
Algorithms, 2018 Extreme learning machine (ELM) is a single hidden layer feedforward neural network (SLFN). Because ELM has a fast speed for classification, it is widely applied in data stream classification tasks.
Rui Yang, Shuliang Xu, Lin Feng
doaj +1 more source
Concept drift detection and adaptation for federated and continual learning [PDF]
Multimedia tools and applications, 2021 Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the ...
F. Casado +5 more
semanticscholar +1 more source