Instruct: Given the title of a paper, retrieve paper titles that are cited by the given paper.
Query: How good is the Electricity benchmark for evaluating concept drift adaptation
Incremental Learning of Concept Drift from Streaming Imbalanced Data
Concept drift detection for online class imbalance learning
The Impact of Diversity on Online Ensemble Learning in the Presence of Concept Drift
Learning drifting concepts: Example selection vs. example weighting
Classifying Data Streams with Skewed Class Distributions and Concept Drifts
Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams
Incremental Evolving Domain Adaptation
Just-in-Time Adaptive Classifiers—Part I: Detecting Nonstationary Changes
Adversarial Adaptation of Synthetic or Stale Data.
Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm
a general framework for mining concept - drifting data streams with skewed distributions .
Cost-Sensitive Perceptron Decision Trees for Imbalanced Drifting Data Streams
Adaptive Model Rules from Data Streams
New Metrics and Experimental Paradigms for Continual Learning
A Drift Detection Method Based on Active Learning
AdaOUBoost: adaptive over-sampling and under-sampling to boost the concept learning in large scale imbalanced data sets
A Dynamic Adaptation of AD-trees for Efficient Machine Learning on Large Data Sets
Drift Compensation for Electronic Nose by Semi-Supervised Domain Adaption
Adaptive Online Learning
Adaptive boosting for transfer learning using dynamic updates
Scientific and human exploration missions enabled by the space launch system block 1B configuration
Detecting Concept Drift with Support Vector Machines
Personality and patterns of Facebook usage
The case against statistical significance testing
A PERFORMANCE EVALUATION OF JPEG STEGANOGRAPHY TECHNIQUES