Learning in the presence of concept drift and hidden contexts
Gerhard Widmer,Miroslav Kubat +1 more
TLDR
A family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear are described, including a heuristic that constantly monitors the system's behavior.Citations
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Journal ArticleDOI
Characterizing Concept Drift
TL;DR: In this paper, the authors present a taxonomy of concept drift types and a framework for quantitative analysis of the types of drift that can occur in machine learning models, and provide a set of formal definitions of different types of concepts of drift.
Journal ArticleDOI
Immune-inspired incremental feature selection technology to data streams
TL;DR: An immune-inspired incremental feature selection algorithm called ISFaiNET is proposed as a solution for mining data streams, immune network memory antibody set which is far less than the size of data streams is design as a sketch data set.
Proceedings ArticleDOI
Adaptive soft sensor for online prediction based on moving window Gaussian process regression
TL;DR: To make GPR model training more efficient, algorithm for variable selection based on Mutual Information is proposed and Prediction capabilities of the proposed method are examined on real industrial data obtained at an oil distillation column.
Proceedings ArticleDOI
Identifying human trafficking patterns online
TL;DR: The focus of this research program will be to expand studies on this subject through the analysis of data in the Spanish language, based on different sources of information, such as social media, dark web, and online newspapers to identify patterns related to human trafficking.
Book ChapterDOI
The Concept of Applying Lifelong Learning Paradigm to Cybersecurity
TL;DR: The concept applying the lifelong learning approach to cybersecurity (attack detection) matches very well to counter current problems in cybersecurity domain, where each new cyber attack can be considered as a new task.
References
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