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class SBC extends AnyRef

Undersampling Based on Clustering. Original paper: "Under-Sampling Approaches for Improving Prediction of the Minority Class in an Imbalanced Dataset" by Show-Jane Yen and Yue-Shi Lee.

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Instance Constructors

  1. new SBC(data: Data, seed: Long = System.currentTimeMillis(), method: String = "NearMiss1", m: Double = 1.0, k: Int = 3, numClusters: Int = 50, restarts: Int = 1, minDispersion: Double = 0.0001, maxIterations: Int = 200, dist: Distance = Distance.EUCLIDEAN, 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

    method

    selection method to apply. Possible options: random, NearMiss1, NearMiss2, NearMiss3, MostDistant and MostFar

    m

    ratio used in the SSize calculation

    k

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

    numClusters

    number of clusters to be created by KMeans core

    restarts

    number of times to relaunch KMeans core

    minDispersion

    stop KMeans core if dispersion is lower than this value

    maxIterations

    number of iterations to be done in KMeans core

    dist

    object of Distance enumeration representing the distance to be used

    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 SBC algorithm.

    Compute the SBC algorithm.

    returns

    undersampled data structure

  7. val dist: Distance
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    Deprecated

    (Since version ) see corresponding Javadoc for more information.

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