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class NM extends AnyRef

NearMiss. Original paper: "kNN Approach to Unbalanced Data Distribution: A Case Study involving Information Extraction" by Jianping Zhang and Inderjeet Mani.

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Instance Constructors

  1. new NM(data: Data, seed: Long = System.currentTimeMillis(), dist: Distance = Distance.EUCLIDEAN, version: Int = 1, nNeighbours: Int = 3, ratio: Double = 1.0, 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

    version

    version of the core to execute

    nNeighbours

    number of neighbours to take for each minority example (only used if version is set to 3)

    ratio

    ratio to know how many majority class examples to preserve. By default it's set to 1 so there will be the same minority class examples as majority class examples. It will take numMinorityInstances * ratio

    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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  6. def compute(): Data

    Compute the NM algorithm.

    Compute the NM algorithm.

    returns

    undersampled data structure

  7. final def eq(arg0: AnyRef): Boolean
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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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