C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Predictive data mining: a practical guide
Predictive data mining: a practical guide
Efficient progressive sampling
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
A Survey of Methods for Scaling Up Inductive Algorithms
Data Mining and Knowledge Discovery
Similarity-Driven Sampling for Data Mining
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Generating Classifier Commitees by Stochastically Selecting both Attributes and Training Examples
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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In the data mining process, it is often necessary to induce classifiers iteratively by the human analysts complete to extract valuable knowledge from data. Therefore, the data mining tools need to extract valid knowledge from a large amount of data quickly enough in response to the human demand. One of the approaches to answer this request is to reduce the training data size by subsampling. In many cases, the accuracy of the induced classifier becomes worse when the training data is subsampled. We propose S3 Bagging (Small SubSampled Bagging) that adopts both subsampling and a method of committee learning, i.e., Bagging. S3Bagging can induce classifier efficiently by reducing the training data size by subsampling and parallel processing. Additionally, the accuracy of the classifier is maintained by aggregating the result of each classifier through the Bagging process. The performance of S3 Bagging is investigated by carefully designed experiments.