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class NCL extends AnyRef

Neighbourhood Cleaning Rule. Original paper: "Improving Identification of Difficult Small Classes by Balancing Class Distribution" by J. Laurikkala.

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Instance Constructors

  1. new NCL(data: Data, seed: Long = System.currentTimeMillis(), dist: Distance = Distance.EUCLIDEAN, k: Int = 3, threshold: Double = 0.5, normalize: Boolean = false, randomData: Boolean = false, verbose: Boolean = false)

    data

    data to work with

    seed

    seed to use. If it is not provided, it will use the system time

    dist

    object of Distance enumeration representing the distance to be used

    k

    number of neighbours to use when computing k-NN rule (normally 3 neighbours)

    threshold

    consider a class to be undersampled if the number of instances of this class is greater than data.size * threshold

    normalize

    normalize the data or not

    randomData

    iterate through the data randomly or not

    verbose

    choose to display information about the execution or not

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  6. def compute(): Data

    Compute the NCL algorithm.

    Compute the NCL algorithm.

    returns

    undersampled data structure

  7. final def eq(arg0: AnyRef): Boolean
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  8. def equals(arg0: Any): Boolean
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  17. final def wait(arg0: Long, arg1: Int): Unit
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  18. final def wait(arg0: Long): Unit
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  19. final def wait(): Unit
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Deprecated Value Members

  1. def finalize(): Unit
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    Deprecated

    (Since version ) see corresponding Javadoc for more information.

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