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Abstract: . Many factors influence a learning process and the performance of a learned classifier. In this paper we investigate the performance effects of class distribution in the training set. We also study different methods of measuring performance based on cost models and the performance effects of training class distribution with respect to the different cost models. Observations from these effects help us devise a distributed multi-classifier meta-learning approach to learn in domains with skewed... (Update)
Context of citations to this paper: More
.... detection domain is extremely dependent on the dollar amount of each credit card transaction, Chan et al. in their studies [12] and [13] represented the cost model in terms of overheads, which are equivalent to operational costs that is needed for each investigation and...
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BibTeX entry: (Update)
P. Chan and S. Stolfo. Learning with Non-uniform Class and Cost Distributions: Effects and a Distributed Multi-Classifier Approach. In Workshop Notes KDD-98 Workshop on Distributed Data Mining, pages 1-9, 1998. http://citeseer.ist.psu.edu/article/chan98learning.html More
@misc{ chan98learning,
author = "P. Chan and S. Stolfo",
title = "Learning with Non-uniform Class and Cost Distributions: Effects and a Distributed
Multi-Classifier Approach",
text = "P. Chan and S. Stolfo. Learning with Non-uniform Class and Cost Distributions:
Effects and a Distributed Multi-Classifier Approach. In Workshop Notes KDD-98
Workshop on Distributed Data Mining, pages 1-9, 1998.",
year = "1998",
url = "citeseer.ist.psu.edu/article/chan98learning.html" }
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