Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Speeding Up the Search for Optimal Partitions
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Learning drifting concepts: Example selection vs. example weighting
Intelligent Data Analysis
Learning from Data Streams: Synopsis and Change Detection
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
Monitoring incremental histogram distribution for change detection in data streams
Sensor-KDD'08 Proceedings of the Second international conference on Knowledge Discovery from Sensor Data
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In this paper we present a method for incremental discretization able to be adapted to gradual changes in the target concept. The proposed method is based on the Partition incremental Discretization (PiD for short). The algorithm divides the discretization task in two layers. The first layer receives the sequence of input data and retains some statistics of the data using more intervals than required. The second layer computes the final discretization, based in the statistics stored by the first layer. The method is able to process streaming examples in a single scan, in constant time and space even for infinite sequences of examples. In dynamic environments the target concept can gradually change over time. Past examples may not reflect the actual status of the problem. To accommodate concept drift we use an exponential decay that smoothly reduces the importance of older examples. Experimental evaluation on a benchmark problem for drift environments, clearly illustrates the benefits of the weighting examples technique.