c

undersampling.core

UndersamplingBasedClustering

class UndersamplingBasedClustering extends Algorithm

Undersampling Based on Clustering algorithm. 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 UndersamplingBasedClustering(data: Data, seed: Long = System.currentTimeMillis(), minorityClass: Any = -1)

    data

    data to work with

    seed

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

    minorityClass

    indicates the minority class. If it's set to -1, it will set to the one with less instances

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  14. def sample(file: Option[String] = None, method: String = "random", m: Double = 1.0, k: Int = 3, numClusters: Int = 50, restarts: Int = 1, minDispersion: Double = 0.0001, maxIterations: Int = 200): Data

    Undersampling method based in SBC

    Undersampling method based in SBC

    file

    file to store the log. If its set to None, log process would not be done

    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 algorithm

    restarts

    number of times to relaunch KMeans algorithm

    minDispersion

    stop KMeans algorithm if dispersion is lower than this value

    maxIterations

    number of iterations to be done in KMeans algorithm

    returns

    Data structure with all the important information

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  1. def finalize(): Unit
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    Deprecated

    (Since version ) see corresponding Javadoc for more information.

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