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DDD: A New Ensemble Approach for Dealing with Concept Drift
TLDR
This paper presents an analysis of low and high diversity ensembles combined with different strategies to deal with concept drift and proposes a new approach to handle drifts. Expand
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The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
TLDR
Online learning algorithms often have to operate in the presence of concept drift (i.e., the concepts to be learned can change with time). Expand
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Ensemble learning for data stream analysis: A survey
TLDR
A comprehensive survey of ensemble approaches for data stream analysis.Taxonomy of ensemble algorithms for various data stream mining tasks.Discussion of open research problems. Expand
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Resampling-Based Ensemble Methods for Online Class Imbalance Learning
TLDR
We give the first comprehensive analysis of class imbalance in data streams, in terms of data distributions, imbalance rates and changes in class imbalance status. Expand
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Ensembles and locality: Insight on improving software effort estimation
TLDR
A principled and extensive analysis of several automated ensembles, single learning machines and locality approaches, which present features potentially beneficial for SEE, is performed. Expand
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Software effort estimation as a multiobjective learning problem
TLDR
A multiobjective evolutionary algorithm (MOEA) is used to better understand the tradeoff among different performance measures by creating SEE models through the simultaneous optimisation of these measures. Expand
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A learning framework for online class imbalance learning
TLDR
We propose a learning framework for online class imbalance learning that decomposes the learning task into three modules. Expand
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Concept drift detection for online class imbalance learning
TLDR
We analyse the impact of concept drift on single-class performance of online models based on three types of classifiers, under seven different scenarios with the presence of class imbalance. Expand
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The Handbook of Engineering Self-Aware and Self-Expressive Systems
TLDR
We proposed a pattern driven methodology for engineering self-aware and self-expressive systems to assist in utilising the patterns and primitives during design. Expand
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A Systematic Study of Online Class Imbalance Learning With Concept Drift
TLDR
We present the first systematic study of handling concept drift in class-imbalanced data streams with concept drift. Expand
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