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m_allowNegativeValues
- Variable in class weka.filters.supervised.instance.
DistributionBasedBalance
indicate if negative values are to be sampled. if false, sampled values are 0 as minimum
m_allowPoissonApproximation
- Variable in class weka.filters.supervised.instance.
DistributionBasedBalance
indicates if sampling from a poisson distribution can be using an approximate way which reduces the sampling time and gets similar sampled values
m_balanceType
- Variable in class weka.filters.supervised.instance.
DistributionBasedBalance
distribution to learn in order to re-sample new instances
m_labelsRange
- Variable in class weka.filters.supervised.instance.
DistributionBasedBalance
range of selected label indexes to balance
m_P
- Variable in class weka.filters.supervised.instance.
DistributionBasedBalance
number P of instances to re-sample per class label
m_samplingTime_ms
- Variable in class weka.filters.supervised.instance.
DistributionBasedBalance
stores the time spent (milliseconds) in sammpling the new instances
m_seed
- Variable in class weka.filters.supervised.instance.
DistributionBasedBalance
seed use for numbers generation
m_statisticsTime_ms
- Variable in class weka.filters.supervised.instance.
DistributionBasedBalance
stores the time spent (milliseconds) in learning the distribution
m_totalInstancesPerClass
- Variable in class weka.filters.supervised.instance.
DistributionBasedBalance
used when MULTINOMIAL_BALANCE is selected
main(String[])
- Static method in class weka.filters.supervised.instance.
DistributionBasedBalance
Main method for running this filter.
MULTINOMIAL_BALANCE
- Static variable in class weka.filters.supervised.instance.
DistributionBasedBalance
learn and sample Multinomial Distribution
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