C4.5: programs for machine learning
C4.5: programs for machine learning
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining time-changing data streams
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Accurate decision trees for mining high-speed data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Moderated VFDT in stream mining using adaptive tie threshold and incremental pruning
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Incrementally optimized decision tree for noisy big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Incrementally optimized decision tree for noisy big data
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
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Big data has become a significant problem in software applications nowadays. Extracting classification model from such data requires an incremental learning process. The model should update when new data arrive, without re-scanning historical data. A single-pass algorithm suits continuously arrival data environment. However, one practical and important aspect that has gone relatively unstudied is noisy data streams. Such data are inevitable in real-world applications. This paper presents a new classification model with a single decision tree, so called incrementally Optimised Very Fast Decision Tree iOVFDT that embeds multi-objectives incremental optimisation and functional tree leaf. In the performance evaluation, noisy values were added into synthetic data. This evaluation investigated the performance under noisy data scenario. The result showed that iOVFDT outperforms the existing algorithms.