Improve the results of MAHOUT-145 by uniformly distributing the classes in the
partitioned data
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Key: MAHOUT-216
URL: https://issues.apache.org/jira/browse/MAHOUT-216
Project: Mahout
Issue Type: Improvement
Components: Classification
Reporter: Deneche A. Hakim
Assignee: Deneche A. Hakim
the poor results of the partial decision forest implementation may be explained
by the particular distribution of the partitioned data. For example, if a
partition does not contain any instance of a given class, the decision trees
built using this partition won't be able to classify this class.
According to [CHAN, 95]:
{quote}
Random Selection of the partitioned data sets with a uniform distribution of
classes is perhaps the most sensible solution. Here we may attempt to maintain
the same frequency distribution over the ''class attribute" so that each
partition represents a good but a smaller model of the entire training set
{quote}
[CHAN, 95]: Philip K. Chan, "On the Accuracy of Meta-learning for Scalable Data
Mining"
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