Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Combining proactive and reactive predictions for data streams
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Suppressing model overfitting in mining concept-drifting data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
StreamMiner: a classifier ensemble-based engine to mine concept-drifting data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Boosting classifiers for drifting concepts
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Pattern change discovery between high dimensional data sets
Proceedings of the 20th ACM international conference on Information and knowledge management
Identification and visualisation of pattern migrations in big network data
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Change-point detection with feature selection in high-dimensional time-series data
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We propose a formulation of a new problem, which we call change analysis, and a novel method for solving the problem. In contrast to the existing methods of change (or outlier) detection, the goal of change analysis goes beyond detecting whether or not any changes exist. Its ultimate goal is to find the explanation of the changes.While change analysis falls in the category of unsupervised learning in nature, we propose a novel approach based on supervised learning to achieve the goal. The key idea is to use a supervised classifier for interpreting the changes. A classifier should be able to discriminate between the two data sets if they actually come from two different data sources. In other words, we use a hypothetical label to train the supervised learner, and exploit the learner for interpreting the change. Experimental results using real data show the proposed approach is promising in change analysis as well as concept drift analysis.