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Abstract: Data mining algorithmsincluding machine learning, statistical analysis, and pattern recognition techniques can greatly improve our understanding of data warehouses that are now becoming more widespread. In this paper, we focus on classification algorithms and review the need for multiple classification algorithms. We describe a system called MLC ++ , which was designed to help choose the appropriate classification algorithm for a given dataset by making it easy to compare the utility of... (Update)
Context of citations to this paper: More
.... of tools for visualizing or interactively exploring the results of learning (e.g. The MineSet Tree Visualizer Kohavi, Sommerfield, Dougherty, 1996). While these tools provide an excellent means of identifying and exploring what was learned, they do not provide...
...from demographic variables. When we evaluate models produced for this task, we would like to obtain rules such as: Classifier MC4 (Kohavi et al. 1997) is 21 less accurate than average on people who are between 45 and 55 years of age, are high school graduates, and are married....
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BibTeX entry: (Update)
R. Kohavi, D. Sommerfield, and J. Dougherty. Data mining using mlc + +, a machine learning library in c + +. International Journal of Artificial Intelligence Tools,, 6(4):537--566, 1997. http://citeseer.ist.psu.edu/article/kohavi96data.html More
@inproceedings{ kohavi96data,
author = "Ron Kohavi and Dan Sommerfield and James Dougherty",
title = "Data Mining Using {MLC}++: {A} Machine Learning Library in {C}++",
booktitle = "Tools with Artificial Intelligence",
publisher = "IEEE Computer Society Press",
pages = "234--245",
year = "1996",
url = "citeseer.ist.psu.edu/article/kohavi96data.html" }
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